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< Back Surpassing Legacy Standards Jan 12, 2026 1. Introduction Some surveillance vendors claim they can automatically detect and classify humans and vehicles at extreme distances—sometimes 1 to 6 miles —using only a few pixels per target. In some cases, claims as low as 2–100 pixels are made. These claims are often justified by citing DRI (Detection, Recognition, Identification) or DORI tables, which were created decades ago for human observers , not autonomous computer-vision systems. At the same time, some vendors attempt to avoid AI and machine learning altogether, relying instead on simple motion detection, thresholding, or rule-based analytics, while still claiming “automatic detection.” Both approaches— misusing DRI/DORI or avoiding ML entirely —lead to systems that fail in real-world deployments. These claims conflict with the physics of imaging , the limitations of sensors , and the fundamental requirements of modern machine-learning (ML) —especially in environments where atmospheric turbulence, reduced contrast, camera shake, background complexity, partial occlusions, animals, and environmental motion are common. This white paper explains why DRI and DORI apply only to human perception, why they cannot be used to predict autonomous classification performance, why ML systems require substantially more pixels on target , and why systems that do not use AI/ML suffer from unacceptably high false-alarm rates . It also explains why long-range conditions require even more margin, and why no company can bypass physics with software. Any extraordinary claim must be validated through a Proof of Concept (POC) . 2. What DRI and DORI Actually Measure 2.1 DRI (Detection, Recognition, Identification) DRI was developed in 1958 to estimate how far a human observer could visually interpret a target using optical or thermal equipment. It describes whether a person can detect that “something is there,” recognize a general category (such as human versus vehicle), or identify a specific type. Humans can often recognize a person with very limited visual information—on the order of 12–16 vertical pixels , which might correspond to roughly: 12 pixels high × ~4 pixels wide ≈ ~48 total pixels , or 16 pixels high × ~5 pixels wide ≈ ~80 total pixels This is possible because the human brain can infer missing detail, guess intent, and apply context. DRI was never designed to evaluate autonomous systems. 2.2 DORI (IEC 62676-4:2015) DORI extends similar ideas to CCTV system design and again describes what a human operator can interpret when viewing video. Recognition-level DORI values often correspond to 7–12 pixels across the target width , still assuming a human is making the judgment. Neither DRI nor DORI evaluates whether a computer can autonomously classify a target, nor do they account for turbulence, camera shake, background complexity, camouflage, or occlusions. 3. Why DRI/DORI Cannot Be Applied to Machine Learning Machine-learning systems such as Convolutional Neural Networks (CNNs) and Transformers classify objects by extracting visual features from the image, including shape, edges, texture gradients, motion consistency, and frame-to-frame stability. If these features do not physically exist in the pixels, the ML system cannot classify the object. For example, a person appearing 10 pixels high × ~3 pixels wide ≈ ~30 total pixels does not contain enough information to reliably determine head shape, limb movement, torso structure, or vehicle geometry. A human observer might guess; an algorithm cannot. DRI/DORI recognition thresholds describe what humans can guess from incomplete data. ML systems require real, measurable information. 4. What Happens If You Do NOT Use AI / Machine Learning It is equally important to understand the consequences of not using AI/ML at all . Systems that rely solely on traditional video analytics—such as simple motion detection, pixel change thresholds, background subtraction, or rule-based logic—lack the ability to understand what is moving. They can detect motion, but they cannot reliably classify it. As a result, non-AI systems typically suffer from: Extremely high false-alarm rates Inability to distinguish humans from animals Inability to reject nuisance motion Poor scalability to large or complex environments 4.1 Why Non-AI Systems Generate Excessive False Alarms Without ML classification, a system must alarm on any motion that meets basic criteria. This includes: Animals Blowing vegetation Shadows Clouds and moving sun patterns Heat shimmer and atmospheric turbulence Camera shake Insects and birds Rain, snow, and dust Rule-based filters can reduce some noise, but they quickly break down in real environments because natural motion is highly variable. As thresholds are tightened to reduce false alarms, real threats are missed. As thresholds are loosened to avoid misses, false alarms explode. This tradeoff cannot be solved without classification. 4.2 Non-AI Systems Cannot Scale As coverage areas grow larger or more complex, non-AI systems become unmanageable: Operators are overwhelmed by alarms Alarm fatigue sets in Systems are ignored or turned down Real threats are lost in noise In practice, many non-AI deployments are eventually disabled or relegated to “monitoring only” because they generate too many alarms to be useful. 4.3 Detection Without Classification Is Operationally Dangerous A system that “detects motion” but cannot determine whether the object is a human, vehicle, animal, or irrelevant noise is not an autonomous security system. It simply shifts the burden to the operator, increasing workload and increasing the chance of human error. This is why modern perimeter security requires both detection and classification , and why AI/ML—used correctly and within physical limits—is essential. 5. Long-Range Physics Further Increase ML Requirements (1–6 Miles) At long ranges, multiple physical effects degrade imagery beyond what DRI/DORI assume: Reduced contrast Background complexity Atmospheric turbulence Camera shake Loss of gradients PureTech mitigates camera-induced motion by performing its proprietary image stabilization as the first processing step , ensuring downstream analytics operate on a stable image. Even so, long-range ML classification requires more pixels , not fewer. 6. Occlusions: Why Real-World Systems Must Design for More Pixel Margin Real environments include frequent occlusions caused by vegetation, terrain, infrastructure, and partial self-occlusion. When only part of a target is visible, the effective usable pixel count drops sharply. For example: 40 px high × 15 px wide ≈ 600 total pixels may be sufficient for a fully visible person. Seeing only half the body may require significantly more total pixels to maintain classification confidence. Designing only to ideal conditions guarantees failure. 7. Independent Evidence: Pixel Requirements for Reliable Autonomous Classification Independent research and industry experience consistently show that reliable autonomous classification cannot be achieved with only a handful of pixels , regardless of algorithm choice or marketing claims. In practical deployments, autonomous classification systems must achieve high probability of correct classification , low false-alarm rates , and low misclassification rates simultaneously. Achieving all three requires substantial spatial and temporal information about the target. Across a wide range of studies and real-world deployments, several consistent observations emerge: Very small targets (on the order of only tens of total pixels) do not contain sufficient structure for reliable autonomous classification. As pixel counts increase into the hundreds of total pixels , classification accuracy improves substantially, particularly when combined with temporal information such as motion consistency. At long ranges, additional factors—including atmospheric turbulence, reduced contrast, background complexity, and partial occlusions—further reduce usable information, increasing the amount of image data (pixels) required to maintain high accuracy. Importantly, there is no single universal pixel threshold that guarantees reliable classification at long range. The effective pixel requirement depends on multiple factors, including sensor modality, environmental conditions, target contrast, degree of occlusion, and system architecture. Systems that rely primarily on static image appearance and single-frame analysis tend to require significantly larger target images (in the thousands) to achieve acceptable performance under degraded conditions. More advanced systems that exploit stabilized imagery, coherent motion over time, and real-world constraints can extract more information from the same imagery—but no credible system can achieve reliable autonomous classification at DRI/DORI recognition levels or at 2 to 10s of total pixels . For long-range applications, it is realistic to expect that classification accuracy improves as available target information (pixel count) grows from a few tens of pixels into the hundreds or more , depending on conditions. Claims of reliable classification far below this regime are not supported by physics, industry experience, or independent research. 8. Training Data: Why Good ML Requires Large, Clean, Real-World Datasets ML performance depends heavily on training data quality and quantity. Modern vision models typically require hundreds of thousands to millions of representative examples. PureTech has been training visible and thermal ML models for 8 years , using hundreds of thousands of real-world images collected under operational conditions. Garbage In, Garbage Out Poor training data leads directly to poor performance. Garbage data includes: Targets that are too small Unrealistic close-ups never seen in deployment Low-contrast imagery Partial fragments without sufficient structure Severe blur or turbulence distortion Incorrect or inconsistent labeling PureTech applies proprietary preprocessing and quality controls to prevent such data from contaminating training. 9. Thermal vs. Visible Imaging Thermal imaging often outperforms visible cameras at long range and at night because it measures emitted heat rather than reflected light. Advantages include better target-background separation, no need for lighting, reduced impact from shadows, and reduced effectiveness of visual camouflage. Thermal does not eliminate physics limits, but it improves signal quality under difficult conditions. 10. MWIR, LWIR, and SWIR Overview LWIR (8–14 µm): uncooled, durable, good short- to medium-range performance MWIR (3–5 µm): superior long-range performance, higher contrast, requires cooling SWIR (~1–2 µm): reflected-light imaging, good detail in low light, poor in fog or total darkness Each has tradeoffs; none can violate physics. 11. PureTech’s Physics-Aligned Multi-Cue Approach PureTech Systems combines: Image stabilization (first step) Terrain-mapped object tracking for real-world size, speed, and direction Motion consistency filtering Shape plausibility checks Speed profiling Contextual and trajectory filtering ML classification applied PureTech holds 16 issued patents covering image processing, stabilization, and computer vision. 12. Why This Matters: Missed Detections, False Alarms, and ROI A missed detection can mean loss of life, loss of critical infrastructure, regulatory penalties, lawsuits, and reputational damage. False alarms waste time, consume resources, cause alarm fatigue, and obscure real threats. Excessive false alarms are functionally equivalent to missed detections because operators stop responding appropriately. 1. Introduction Some surveillance vendors claim they can automatically detect and classify humans and vehicles at extreme distances—sometimes 1 to 6 miles —using only a few pixels per target. In some cases, claims as low as 2–100 pixels are made. These claims are often justified by citing DRI (Detection, Recognition, Identification) or DORI tables, which were created decades ago for human observers , not autonomous computer-vision systems. At the same time, some vendors attempt to avoid AI and machine learning altogether, relying instead on simple motion detection, thresholding, or rule-based analytics, while still claiming “automatic detection.” Both approaches— misusing DRI/DORI or avoiding ML entirely —lead to systems that fail in real-world deployments. These claims conflict with the physics of imaging , the limitations of sensors , and the fundamental requirements of modern machine-learning (ML) —especially in environments where atmospheric turbulence, reduced contrast, camera shake, background complexity, partial occlusions, animals, and environmental motion are common. This white paper explains why DRI and DORI apply only to human perception, why they cannot be used to predict autonomous classification performance, why ML systems require substantially more pixels on target , and why systems that do not use AI/ML suffer from unacceptably high false-alarm rates . It also explains why long-range conditions require even more margin, and why no company can bypass physics with software. Any extraordinary claim must be validated through a Proof of Concept (POC) . 2. What DRI and DORI Actually Measure 2.1 DRI (Detection, Recognition, Identification) DRI was developed in 1958 to estimate how far a human observer could visually interpret a target using optical or thermal equipment. It describes whether a person can detect that “something is there,” recognize a general category (such as human versus vehicle), or identify a specific type. Humans can often recognize an object is a person with very limited visual information—on the order of 12–16 vertical pixels , which might correspond to roughly: 12 pixels high × ~4 pixels wide ≈ ~48 total pixels , or 16 pixels high × ~5 pixels wide ≈ ~80 total pixels This is possible because the human brain can infer missing detail, guess intent, and apply context. DRI was never designed to evaluate autonomous systems. 2.2 DORI (IEC 62676-4:2015) DORI extends similar ideas to CCTV system design and again describes what a human operator can interpret when viewing video. Recognition-level DORI values often correspond to 7–12 pixels across the target width , still assuming a human is making the judgment. Neither DRI nor DORI evaluates whether a computer can autonomously classify a target, nor do they account for turbulence, camera shake, background complexity, camouflage, or occlusions. 3. Why DRI/DORI Cannot Be Applied to Machine Learning Machine-learning systems such as Convolutional Neural Networks (CNNs) and Transformers classify objects by extracting visual features from the image, including shape, edges, texture gradients, motion consistency, and frame-to-frame stability. If these features do not physically exist in the pixels, the ML system cannot classify the object. For example, a person appearing 10 pixels high × ~3 pixels wide ≈ ~30 total pixels does not contain enough information to reliably determine head shape, limb movement, torso structure, or vehicle geometry. A human observer might guess; an algorithm cannot. DRI/DORI recognition thresholds describe what humans can guess from incomplete data. ML systems require real, measurable information. 4. What Happens If You Do NOT Use AI / Machine Learning It is equally important to understand the consequences of not using AI/ML at all . Systems that rely solely on traditional video analytics—such as simple motion detection, pixel change thresholds, background subtraction, or rule-based logic—lack the ability to understand what is moving. They can detect motion, but they cannot reliably classify it. As a result, non-AI systems typically suffer from: Extremely high false-alarm rates Inability to distinguish humans from animals Inability to reject nuisance motion Poor scalability to large or complex environments 4.1 Why Non-AI Systems Generate Excessive False Alarms Without ML classification, a system must alarm on any motion that meets basic criteria. This includes: Animals Blowing vegetation Shadows Clouds and moving sun patterns Heat shimmer and atmospheric turbulence Camera shake Insects and birds Rain, snow, and dust Rule-based filters can reduce some noise, but they quickly break down in real environments because natural motion is highly variable. As thresholds are tightened to reduce false alarms, real threats are missed. As thresholds are loosened to avoid misses, false alarms explode. This tradeoff cannot be solved without classification. 4.2 Non-AI Systems Cannot Scale As coverage areas grow larger or more complex, non-AI systems become unmanageable: Operators are overwhelmed by alarms Alarm fatigue sets in Systems are ignored or turned down Real threats are lost in noise In practice, many non-AI deployments are eventually disabled or relegated to “monitoring only” because they generate too many alarms to be useful. 4.3 Detection Without Classification Is Operationally Dangerous A system that “detects motion” but cannot determine whether the object is a human, vehicle, animal, or irrelevant noise is not an autonomous security system. It simply shifts the burden to the operator, increasing workload and increasing the chance of human error. This is why modern perimeter security requires both detection and classification , and why AI/ML—used correctly and within physical limits—is essential. 5. Long-Range Physics Further Increase ML Requirements (1–6 Miles) At long ranges, multiple physical effects degrade imagery beyond what DRI/DORI assume: Reduced contrast Background complexity Atmospheric turbulence Camera shake Loss of gradients PureTech mitigates camera-induced motion by performing its proprietary image stabilization as the first processing step , ensuring downstream analytics operate on a stable image. Even so, long-range ML classification requires more pixels , not fewer. 6. Occlusions: Why Real-World Systems Must Design for More Pixel Margin Real environments include frequent occlusions caused by vegetation, terrain, infrastructure, and partial self-occlusion. When only part of a target is visible, the effective usable pixel count drops sharply. For example: 40 px high × 15 px wide ≈ 600 total pixels may be sufficient for a fully visible person. Seeing only half the body may require significantly more total pixels to maintain classification confidence. Designing only to ideal conditions guarantees failure. 7. Independent Evidence: Pixel Requirements for Reliable Autonomous Classification Independent research and industry experience consistently show that reliable autonomous classification cannot be achieved with only a handful of pixels , regardless of algorithm choice or marketing claims. In practical deployments, autonomous classification systems must achieve high probability of correct classification , low false-alarm rates , and low misclassification rates simultaneously. Achieving all three requires substantial spatial and temporal information about the target. Across a wide range of studies and real-world deployments, several consistent observations emerge: Very small targets (on the order of only tens of total pixels) do not contain sufficient structure for reliable autonomous classification. As pixel counts increase into the hundreds of total pixels , classification accuracy improves substantially, particularly when combined with temporal information such as motion consistency. At long ranges, additional factors—including atmospheric turbulence, reduced contrast, background complexity, and partial occlusions—further reduce usable information, increasing the amount of image data (pixels) required to maintain high accuracy. Importantly, there is no single universal pixel threshold that guarantees reliable classification at long range. The effective pixel requirement depends on multiple factors, including sensor modality, environmental conditions, target contrast, degree of occlusion, and system architecture. Systems that rely primarily on static image appearance and single-frame analysis tend to require significantly larger target images (in the thousands) to achieve acceptable performance under degraded conditions. More advanced systems that exploit stabilized imagery, coherent motion over time, and real-world constraints can extract more information from the same imagery—but no credible system can achieve reliable autonomous classification at DRI/DORI recognition levels or at 2 to 10s of total pixels . For long-range applications, it is realistic to expect that classification accuracy improves as available target information (pixel count) grows from a few tens of pixels into the hundreds or more , depending on conditions. Claims of reliable classification far below this regime are not supported by physics, industry experience, or independent research. 8. Training Data: Why Good ML Requires Large, Clean, Real-World Datasets ML performance depends heavily on training data quality and quantity. Modern vision models typically require hundreds of thousands to millions of representative examples. PureTech has been training visible and thermal ML models for 8 years , using hundreds of thousands of real-world images collected under operational conditions. Garbage In, Garbage Out Poor training data leads directly to poor performance. Garbage data includes: Targets that are too small Unrealistic close-ups never seen in deployment Low-contrast imagery Partial fragments without sufficient structure Severe blur or turbulence distortion Incorrect or inconsistent labeling PureTech applies proprietary preprocessing and quality controls to prevent such data from contaminating training. 9. Thermal vs. Visible Imaging Thermal imaging often outperforms visible cameras at long range and at night because it measures emitted heat rather than reflected light. Advantages include better target-background separation, no need for lighting, reduced impact from shadows, and reduced effectiveness of visual camouflage. Thermal does not eliminate physics limits, but it improves signal quality under difficult conditions. 10. MWIR, LWIR, and SWIR Overview LWIR (8–14 µm): uncooled, durable, good short- to medium-range performance MWIR (3–5 µm): superior long-range performance, higher contrast, requires cooling SWIR (~1–2 µm): reflected-light imaging, good detail in low light, poor in fog or total darkness Each has tradeoffs; none can violate physics. 11. PureTech’s Physics-Aligned Multi-Cue Approach PureTech Systems combines: Image stabilization (first step) Terrain-mapped object tracking for real-world size, speed, and direction Motion consistency filtering Shape plausibility checks Speed profiling Contextual and trajectory filtering ML classification applied PureTech holds 16 issued patents covering image processing, stabilization, and computer vision. 12. Why This Matters: Missed Detections, False Alarms, and ROI A missed detection can mean loss of life, loss of critical infrastructure, regulatory penalties, lawsuits, and reputational damage. False alarms waste time, consume resources, cause alarm fatigue, and obscure real threats. Excessive false alarms are functionally equivalent to missed detections because operators stop responding appropriately. Organizations that choose systems based solely on lowest acquisition cost often incur far higher total cost of ownership and risk exposure. Investing upfront in systems designed around physics, robust ML, stabilization, terrain mapping, and multi-cue validation delivers far better ROI by avoiding catastrophic failures and operational collapse. 13. Proof of Concept: The Only Valid Verification Any vendor claiming autonomous classification at DRI/DORI pixel levels, at less than several hundred total pixels , especially under extreme long-range and occluded conditions must demonstrate the claim in a Proof of Concept . Physics always wins. 14. Conclusion DRI and DORI describe what humans can infer. They do not describe what autonomous systems require. Systems that ignore ML generate unacceptable false alarms. Systems that misuse ML or ignore physics miss real threats. PureTech Systems delivers reliable autonomous detection and classification by respecting physical reality, using stabilized imagery, terrain-mapped measurements, multi-cue analytics, disciplined ML training, and patented computer-vision technology—producing operational security systems that actually work as demonstrated by its real-world deployments in the most challenging environments such as country borders. Organizations that choose systems based solely on lowest acquisition cost often incur far higher total cost of ownership and risk exposure. Investing upfront in systems designed around physics, robust ML, stabilization, terrain mapping, and multi-cue validation delivers far better ROI by avoiding catastrophic failures and operational collapse. 13. Proof of Concept: The Only Valid Verification Any vendor claiming autonomous classification at DRI/DORI pixel levels, at less than several hundred total pixels , especially under extreme long-range and occluded conditions must demonstrate the claim in a Proof of Concept . Physics always wins. 14. Conclusion DRI and DORI describe what humans can infer. They do not describe what autonomous systems require. Systems that ignore ML generate unacceptable false alarms. Systems that misuse ML or ignore physics miss real threats. PureTech Systems delivers reliable autonomous detection and classification by respecting physical reality, using stabilized imagery, terrain-mapped measurements, multi-cue analytics, disciplined ML training, and patented computer-vision technology—producing operational security systems that actually work as demonstrated by its real-world deployments in the most challenging environments such as country borders. Previous Next

< Back PureTech Systems Announces the Release of PureActiv® Version 16 Nov 14, 2024 Phoenix, AZ – PureTech Systems Inc., a leader in geospatial AI-boosted video analytics for wide-area perimeter and border security, is proud to announce the release of PureActiv® Version 16. This new version introduces advanced features aimed at providing nuisance alarm elimination, and autonomous perimeter detection, classification, tracking, alerting, and deterrence—designed to address the evolving security needs of critical infrastructure. PureActiv® Version 16 leverages PureTech’s patented geospatial AI-boosted technology, delivering accuracy in detecting and classifying potential threats with near-zero nuisance alarms. One of the new capabilities in Version 16 includes enhanced machine learning (ML) models that significantly improve classification accuracy, allowing for precise differentiation between real access control events and faulty door switches/locks. Key features include: Enhanced ML Models : Improved classification accuracy, ensuring real access control events are confirmed while nuisance alarms are automatically rejected. PTZ Camera Tracking of Specific Intruders : Intelligent tracking of specific intruders with Pan-Tilt-Zoom (PTZ) cameras for real-time situational awareness and response. Access Control Real and Nuisance alarm differentiation plus auto-clearing. Integrated Air and Ground Intrusion Detection : Integration of ground and counter-drone detection into a single operating picture, providing protection across land, maritime domain and air. "We are excited to release PureActiv® Version 16 as the next step in autonomous perimeter security," said Larry Bowe, CEO of PureTech Systems Inc. "With enhanced machine learning models, advanced PTZ camera tracking, and integrated detection capabilities, PureActiv® continues to set the standard for protecting borders and critical infrastructures." “This new release enables new autonomous capabilities that go beyond conventional measures, providing operational advantages and seamlessly blending with other integrated technology systems,” says Chris Sincock, VP of Critical Infrastructure. For more information on PureActiv® Version 16 and its new security capabilities, visit www.puretechsystems.com or contact us at 602-424-9842. Previous Next

< Back PureTech Honored with 2020 ‘ASTORS’ Homeland Security Awards Dec 10, 2020 PHOENIX, Ariz. – PureTech Systems today announced it is the recipient of five (5) coveted 2020 ‘ASTORS’ Homeland Security Awards from American Security Today for its patented Video Analytics technology, PureActiv®. The Annual ‘ASTORS’ Awards, now in its fifth year, is the preeminent U.S. Homeland Security Awards Program, highlighting the most cutting-edge and forward-thinking security solutions coming onto the market today. “2020 has been a very challenging year for everyone due to the COVID-19 pandemic however, the 2020 ‘ASTORS’ Homeland Security Awards Program was again a huge success and many new categories were added including a section for COVID-19 Detection and Innovation,” said Michael Madsen, co-founder and publisher of American Security Today. The program is specifically designed to honor distinguished government and vendor solutions that deliver enhanced value, benefit and intelligence to end users in a variety of government, homeland security, enterprise, and public safety vertical markets. 90% of ‘ASTORS’ Award Winners return to compete in the Annual ‘ASTORS‘ Homeland Security Awards Program, and 100% of ‘ASTORS’ Sponsors have returned year to year to reap the benefits of their participation in the industry’s largest and most comprehensive Annual Awards Program. As the nation continues to respond to escalating threats from home and abroad, the innovative solutions being implemented to meet those threats, has led to tremendous growth in the field of Homeland Security. “Today, the United States is increasingly focusing on protecting public spaces, as well as IT/cyber security networks and they are calling on innovative companies like (‘ASTORS’ Winner) to help them do so,” according to Tammy Waitt, co-founder and editorial director of American Security Today. “‘ASTORS’ nominations are evaluated on their technical innovation, interoperability, specific impact within the category, overall impact to the industry, relatability to other industry technologies, and application feasibility outside of the industry,” concluded Waitt. PureActiv received awards in five (5) categories for its cutting-edge Video Analytics that are AI Deep Learning and Geo-spatially boosted providing real-time detection, classification, and tracking of perimeter security threats. The innovative solution received Platinum Awards for Best Perimeter Protection System, Best Long-Range Surveillance for Border Protection, and Best Machine-Learning and AI Solution. PureActiv® also received a Gold Award for Best Maritime Perimeter Protection System and a Silver Award for Best Video Analytics Solution. PureTech’s PureActiv® AI Video Analytics provide a fully-integrated outdoor wide-area perimeter protection system with the highest probability of detection and the lowest nuisance alarm rates for securing facilities, critical infrastructure, seaports, and borders. “We are honored to be recognized in so many categories for our groundbreaking achievements in American Security Today’s distinguished 2020 ‘ASTORS’ Awards Program for advances in protecting borders and perimeters with AI Technology. These awards demonstrate the 15+ years of hard work and determination that PureTech Systems has dedicated to providing the strongest layered automated detection, classification, tracking, and deterrent solution for protecting any critical infrastructure, facility, or border,” said Larry Bowe, Jr., President of PureTech Systems. To learn more about PureTech Systems solutions for perimeter protection, please visit https://americansecuritytoday.com/ast-proudly-presents-the-2020-astors-awards-winners/ or PureTech’s website at www.puretechsystems.com . Previous Next

Charlie Farnsworth, with over 40 years of experience in the software industry, currently serves as the Operations Manager at PureTech Systems. In this position, Charlie is responsible for... < Back Charlie Farnsworth Operations Manager Charlie Farnsworth, with over 40 years of experience in the software industry, currently serves as the Operations Manager at PureTech Systems. In this position, Charlie is responsible for the company's day-to-day operations, ensuring excellence in customer and technical support. Before joining PureTech, Charlie made substantial contributions to the aerospace and aviation sectors during his tenure at Honeywell Intl Inc. There, he played a crucial role in the development and testing of advanced avionics systems for commercial airliners and business jets. Most recently, his expertise was pivotal in the development of the Orion space capsule, a cornerstone project in deep space exploration where he was awarded EM-1 Orion Program Manager’s Commendation . Recognized for his leadership and dedication to advancing technology, Charlie 's forward-thinking approach and commitment to excellence are a perfect match for the innovative culture at PureTech Systems. Charlie 's holds a Bachelor of Science degree in Mathematics and Geology from Millsaps College, and a Master of Science in Mathematics from Southern Methodist University. Mail Document

< Back Gord Helm, MPA Gord Helm is the former Port of Halifax Manager of Security and Operations. Gord served over 20 years in the Royal Canadian Navy and is active in corporate governance. He currently sits on the Security Advisory Board of Source Security, serves as a Director with SWANA, Nova Scotia Chapter; and is a past Chair, Halifax Port Security Committee and recently completed 6 years as an Executive Board member with the Sacred Heart School of Halifax; and was Chair of the Harbour Operations Committee and Harbour Master for several International Halifax Tall Ship events. Gord holds an under graduate degree in political science and a Master of Public Administration from Dalhousie and a Master Certification in Project Management from St. Mary’s University. He is a certified PMP. Gord currently serves as Chief Technology Officer for Fourth State Energy, a company which delivers renewable energy from waste through plasma gasification. info@mysite.com 123-456-7890

< Back PureTech Systems President, Larry Bowe was featured in The Last Word With… in Security Journal Americas May edition May 5, 2023 PHOENIX, Ariz - PureTech Systems President, Larry Bowe, was featured on The Last Word With... in the May edition of Security Journal Americas. His interview can be read below. Can you tell me about PureTech Systems? PureTech was founded in 2004 – we are a trusted go-to company for organizations needing to secure the perimeter of their critical operations more efficiently and effectively. We provide the most comprehensive, patented, easy to integrate, and reliable AI-boosted, geospatial video analytics and sensor fusion software solution. Our cutting-edge software turns ordinary security cameras and other sensors into autonomous, highly accurate, and scalable perimeter intrusion detection systems to deliver real-time situational awareness, enabling customers to make informed decisions quickly and effectively. We empower our clients to protect their assets, people, and critical infrastructure with confidence while lowering operational costs. What range of markets do you serve? We are typically the chosen solution for wide area and/or high-risk perimeter protection at government borders, electric utilities including distribution, nuclear power plants, passenger rail, water utilities, seaports, and airports. What makes PureTech stand out in the market? PureTech’s PureActiv Software Platform enables the highest probability of detection, with the lowest false alarm rate, by auto-verifying objects of interest using our geospatial, AI boosted video analytics. Essentially, we turn standard security cameras into high-resolution, non-transmitting radars. We deliver the best geospatial situational awareness, faster response, and lower workload due to our automation and Common Operating Picture which brings together legacy and new sensors into a homogonous Single Pane of Glass. Furthermore, we concisely and reliably automate, in real-time, intruder detection, classification and location, video streaming, notifications, and deterrence. With Longer detection ranges (fewer cameras, etc. needed) and reuse of existing sensors (cameras, radar, etc.) PureActiv lowers system acquisition costs. Operators, Agents, Guards, and Police that use PureActiv know they are using a trusted system that improves their performance. "We deliver the best geospatial situational awareness, faster response, and lower workload" Some key capabilities: PureActiv integrates with multiple manufacturers’ ground and air detection radars to locate moving objects in a geospatial range. Once detected by the radar, PureActiv automatically determines if the track is in a location of interest and if so, it controls PTZ cameras, to point at the radar track and zoom appropriately based on the range to the target. PureActiv then leverages its AI-boosted Video Analytics to automatically classify the target. When PureActiv classifies a moving object as a target of interest (person, vehicle, drone), PureActiv initiates several automated actions including: 1) Transmitting classified track metadata to the Command, Control, and Communications System, 2) Display on a GIS map, 3) Adding the track to an alarm que for prioritized handling, 4) Auto following the target with a PTZ camera, and 5) Commanding deterrent devices. Auto-following has two modes; one PureActiv commanding the PTZ camera to continuously slew to the radar track and the other; using PureActiv optical tracking algorithms to follow the target based on the video. This combination of information acts as a tremendous force multiplier by bringing the human in the loop at the appropriate time (once the target is auto verified) with high fidelity intelligence for their actions (next steps). In addition to radars, PureTech Systems is integrated with multiple sensor technologies and systems, including, but not limited to, drones for dispatch to alarms, cameras, GPS, VMS/PSIM, loud hailers, strobe/spotlights, and AIS (ship transponders) – all to provide the most cost-effective total solution possible. There are many customers that use PureActiv as their VMS or PSIM due to our exceptional geographic User Interface. Download a full version of the interview here: SJA - Issue 12 - LastWord .pdf Download PDF • 4.36MB Previous Next

< Back PureTech Wins 6 Awards Dec 1, 2022 PHOENIX, Ariz. – PureTech today announced it is the recipient of multiple 2022 ‘ASTORS’ Homeland Security Awards from American Security Today. PureTech was recognized as platinum award winners for the following categories: PureActiv - Best Artificial Intelligence & Machine Learning Solution PureActiv - Best Video Analytics Solution PureActiv - Best Long Range Surveillance Solution PureActiv - Best Maritime Perimeter Protection Solution In addition to winning platinum honors, PureTech was acknowledged as gold award winners for: PureActiv R-DAPSS - Best Mobile Surveillance ISR Solution PureActiv - Best Perimeter Protection System "We are thrilled to be recognized for our products and solutions, that are at the core of our business” said Larry Bowe, Jr., President of PureTech Systems. The Annual ‘ASTORS’ Awards Program is specifically designed to honor distinguished government and vendor solutions that deliver enhanced value, benefit, and intelligence to end-users in a variety of government, homeland security, enterprise, and public safety vertical markets. “‘ASTORS’ nominations are evaluated on their technical innovation, interoperability, specific impact within the category, overall impact to the industry, relatability to other industry technologies, and application feasibility outside of the industry,” said AST’s Publisher, Michael J. Madsen. __________________________________________________________________________________________ About PureTech Systems® PureTech Systems Inc. is a privately owned company established in 2004 that develops, markets, and supports its patented location-based AI video analytics software, PureActiv©, for real time safety and security applications. The company’s software improves situational awareness with AI video analytics, sensor integration and information fusing for automated real-time event detection and forensic video content analysis with primary emphasis on autonomous intrusion detection of ground and UAS targets and camera tracking for borders and coastlines, facility perimeters and critical infrastructures (pipelines, railroads, dams, bridges, ports, utilities, power plants, military bases, and airports). To find out more about PureTech Systems Inc. visit our website at www.puretechsystems.com , call 602-424-9842 or email info@PureTechSystems.com . About American Security Today American Security Today (AST), the ‘New Face in Homeland Security™’, is the premier digital media platform in the U.S. Homeland Security and Public Safety Industry, focused on breaking news and in-depth coverage of the newest initiatives and hottest technologies in physical & IT security on the market today. AST highlights the most cutting-edge and forward-thinking security solutions across a wide variety of media products delivered daily, weekly, and monthly to over 75,000 qualified government and security industry readers, essential to meeting today’s growing security challenges to ‘Secure our Nation, One City at a Time™’. Previous Next

< Back PureTech Awarded Contract to Protect Additional Power Generation Sites Aug 31, 2020 PHOENIX, Ariz. – PureTech Systems announces it has been awarded another multiple site contract for the deployment of its PureActiv Geospatial AI Video Analytics, multi-Sensor Integration, and Command and Control software. The system will provide wide-area perimeter protection at multiple power generation plants in the United States. This award follows the earlier successful 2020 deployment of PureActiv at multiple Power Generation sites in the U.S. The system integrates PureTech’s market-leading geospatial AI Deep Learning video analytics and other sensor technologies into a seamless Common Operating Picture. The automated system is protecting miles of perimeter from unauthorized intrusion through fence-lines and turnstiles. The additional sites are scheduled to be completed by the end of the year. For security reasons, the client cannot be disclosed. "These deployments demonstrate that our patented perimeter intrusion detection software solution can be successfully deployed on a large scale in a very short time frame" stated Larry Bowe, President of PureTech Systems. "It speaks to the 15 years of investment we have made, not only in market-leading intrusion detection and classification algorithms, but also in ease of deployment and use." Previous Next

< Back PureTech Systems to Showcase Next-Generation Autonomous Perimeter Protection at SIA’s Perimeter PREVENT 2025 Jun 17, 2025 Phoenix, AZ. — PureTech Systems , a leader in autonomous perimeter security solutions, announced its participation at Perimeter PREVENT 2025 , a high-impact security symposium hosted by the Security Industry Association (SIA). The event will be held June 18, 2025, at the National Housing Center in Washington, D.C. PureTech will exhibit at Table #9 , where attendees can explore the company’s advanced, geospatial AI-boosted perimeter detection technologies. PureTech’s participation reinforces its commitment to transforming perimeter protection through autonomy, high-accuracy, near-zero nuisance alarm systems powered by its patented AI-Boosted Geospatial Video Analytics . Built for today’s complex threat environments, PureTech’s technology is trusted to secure facilities, critical infrastructure, and borders, providing unmatched situational awareness and cost-effective operational efficiency. Smarter Surveillance with PureActiv®: Empowering Border and Perimeter Protectio n Controlling national borders and critical perimeters requires a sophisticated combination of trained personnel, precise sensors, automated sensor collaboration, and unified situational awareness. Vast and remote terrains, unpredictable weather, and rapidly evolving threats make this a complex challenge. PureTech Systems delivers a proven solution to meet these demands—providing autonomous threat detection, classification, verification, and deterrence across both new and legacy systems. The system satisfies the U.S. federal government’s definition of Autonomous Systems for border and critical infrastructure protection. PureTech’s award-winning AI-Boosted Geospatial Video Analytics software powers intelligent, real-time monitoring that keeps security teams informed and in control. Our system integrates with a variety of sensor types including radar, thermal, visual, RF, fence sensors, and others allowing for iterative fielding and continuous improvement without the need for costly infrastructure changes. A feature-complete and well documented software Interface enables third parties to integrate PureActiv autonomous perimeter protection into their user interface be it an access control, NVR, PSIM, or other monitoring software. Together, these capabilities create a high-confidence detection and interdiction environment, enabling border and critical infrastructure teams to respond faster, smarter, and with greater accuracy. PureTech’s platform not only reduces false alarms—it delivers actionable intelligence and enables a proactive security posture that scales across fixed installations, remote regions, and rapidly changing threat landscapes. Join PureTech at Perimeter PREVENT Hosted by the Security Industry Association’s Perimeter Security Subcommittee, Perimeter PREVENT gathers security leaders, engineers, integrators, and government personnel to explore strategies in perimeter detection, alerting, and layered defense. Attendees are encouraged to visit Table #9 to learn how PureTech is redefining perimeter protection with scalable, intelligent, and proven technology. National Housing Center – 1201 15th St. NW, Washington, DC 20005 on June 18, 2025. Learn more about the PureTech Systems at our website, www.puretechsystems.com , or stop by our table to speak with our team. Smart Surveillance. Real Results. That’s PureTech. About PureTech Systems Inc. PureTech Systems Inc.® is a privately owned company established in 2004 that develops, markets, and supports its patented location-based AI-boosted video analytics software, PureActiv®, for real time safety and security applications and our SaaS-based PurifAI®. Their primary emphasis is on autonomous perimeter intrusion detection and classification of ground and aerial targets for country borders, coastlines, facility perimeters, and critical infrastructures (pipelines, railroads, dams, bridges, ports, utilities, power plants, military bases, and airports). For media inquiries or to schedule a meeting at the event Contact: info@puretechsystems.com or call 602-424-9842. Previous Next

< Back PureTech Systems Clinches Dual Platinum Govies Awards from SecurityToday Apr 2, 2024 Phoenix, Ariz – PureTech Systems, known for its advanced geospatial AI-boosted video analytics for critical infrastructure and border security, is excited to announce the winning of two Platinum Govies Awards for their PureActiv AlertView Command and Control (COP) and PureActiv geospatial AI-boosted video analytics with Magos Radars. These awards highlight the company's dedication to improving safety and operational effectiveness. The PureActiv AlertView Command and Control (COP) system has earned PureTech Systems the first Platinum Award in the Security Integration Software category. The PureActiv AlertView COP provides security professionals with accurate and reliable real-time alarms, geolocations of live tracks, and video of suspicious activity in outdoor and remote environments while minimizing nuisance alarms. AlertView’s intuitive user-interface highlights PureActiv geospatial AI-boosted video analytics with robust integration features including advanced object detection, AI classification, and tracking displayed on a geographic map, automated camera steering, scalable video distribution, intrusion detection sensor integrations, and automated security policy response. Additionally, in the Perimeter Protection category, the proven combination of PureTech's PureActiv geospatial AI-boosted video analytics with Magos Radars has brought home the second Platinum Award. This marriage of technologies utilizes the location data of a detected object of interest, provided by Magos radars, and PureActiv geospatial AI-boosted video analytics for object classification and to autonomously invoke PTZ auto-follow for continuous tracking, while simultaneously alerting the operator of potential auto-verified threats. This robust layered approach to perimeter and border protection acts as a tremendous force multiplier by filtering out any nuisance alarms prior to bringing a human in the loop. Larry Bowe, President & CEO of PureTech Systems, expresses his enthusiasm: "We are thrilled to be recognized by the Govies Awards from SecurityToday. This honor reflects our continuous drive for innovation and our unwavering commitment to protect people and assets." Adding to the accolades, Yaron Zussman, General Manager of Magos America Inc., commented, "Our alliance with PureTech Systems illustrates the power of combining leading-edge technologies to create superior security solutions. We are honored to be part of this award-winning achievement and look forward to our continued collaboration." The Govies Awards are designed to celebrate outstanding products in government security, spotlighting those that address the specific needs of the public sector. The dual Platinum Awards won by PureTech Systems affirm its position as a frontrunner in delivering innovative security technology. To learn more about PureTech Systems and experience their innovative security solutions firsthand, be sure to visit them at ISC West in Las Vegas, April 9-12. You can find PureTech at booth #7055 , where they will be showcasing their latest advancements in their geospatial AI-boosted video analytics and integrated security technologies. Don't miss this opportunity to engage with the experts and discover how their award-winning solutions can enhance your security operations. See you at ISC West! About PureTech Systems PureTech Systems Inc. is a privately owned company established in 2004 that develops, markets, and supports its patented location-based AI-boosted video analytics software, PureActiv©, for real time safety and security applications. The company's AI-boosted video analytics, paired with numerous sensor integrations and information fusing is all displayed in the PureActiv AlertView, Common Operating Picture. Automated real-time event detection and forensic video content analysis improves users' overall situational awareness. Their primary emphasis is on autonomous perimeter intrusion detection of ground and aerial targets for country borders, coastlines, facility perimeters and critical infrastructures (pipelines, railroads, dams, bridges, ports, utilities, power plants, military bases, and airports). To find out more about PureTech Systems Inc. visit our website at www.puretechsystems.com , call 602-424-9842 or email info@PureTechSystems.com . About Magos Americas Magos Systems was founded in 2010, with a vision to bring advanced radar technologies to the civilian markets. The company offers extensive know-how and specializes in the development of innovative, high-performance, cost-effective radars. Magos' perimeter security solution seamlessly integrates with many VMS and camera models, and together with its unique AI Technology provides exact video-based object classification to cut down nuisance alarms to near zero without compromising threat detection capabilities. With a broad international experience in hundreds of installations for over 40 countries, Magos Systems provides comprehensive and advanced security solutions for multiple verticals. For further information, visit the company's website at: www.magossystems.com . To learn more about Magos, the company will be exhibiting at ISC West booth #28053 . Contact: Megan Willinger Marketing Manager PureTech Systems megan.willinger@puretechsystems.com Previous Next

< Back World Defense Show Post-Show Recap Feb 14, 2024 The recent event in the Kingdom of Saudi Arabia (KSA) was nothing short of remarkable, marking a significant stride in understanding the nuanced landscape of business operations within the region. Central to the discussions and interactions was the theme of localization, a cornerstone of KSA's Vision 2030. This initiative is not just a policy but a mandate for businesses aiming for sustained engagement in the Saudi market. The emphasis on localization underscores a strategic approach to economic diversification and sustainable development, aligning closely with the national vision. A key takeaway from the event is the prevalent strategy among companies to form Joint Ventures (JVs) as a pathway to localization. This approach is widely adopted as it aligns with the regulatory framework and facilitates deeper market penetration. The discussions highlighted the importance of such partnerships, indicating a pivotal topic for future marketing discussions where we can delve deeper into the intricacies and benefits of forming JVs. The commitment of companies to the Saudi market was evident through their substantial investments in exhibiting at the show and participating in public relations events. Notable interactions with local military officials and the US Ambassador underscored the strategic importance of these engagements. Many established companies within border security showcased their dedication to the Saudi market, and their presence, along with that of many emerging players, highlighted a vibrant competitive landscape. These new entrants, often offering competitive pricing and comparable technological capabilities, are reshaping market dynamics. Their flexibility and willingness to conduct extensive in-country demonstrations reflect a keen understanding of the market's unique requirements. This gathering was a testament to the evolving business ecosystem in KSA, characterized by a blend of established firms and new entrants all navigating the localization imperative. As we move forward, the insights gathered from this event will undoubtedly inform our strategies and approaches to engaging with the Saudi market. The conversations and meetings held have laid a solid foundation for future collaborations and business success in alignment with KSA's ambitious Vision 2030. Previous Next

< Back PureTech Systems Featured in Wall Street Journal Coverage on Autonomous Border and Critical Infrastructure Protection May 8, 2026 PureTech Systems was recently featured in a Wall Street Journal article examining the rapid growth of AI-enabled surveillance and autonomous perimeter technologies across the homeland security sector. The article, titled “Trump’s Border Spending Spurs Boom in AI-Infused Surveillance,” explores how advances in artificial intelligence and integrated surveillance platforms are reshaping operations where the stakes of an undetected intrusion are at their highest—and can lead to catastrophic consequences. During the feature, PureTech Systems CEO Larry Bowe discussed the increasing role of AI in modern border and critical infrastructure protection operations and how autonomous software provides the precision necessary to complement existing hardware. “A lot of it is very complementary,” said Larry Bowe, chief executive of PureTech Systems. “You can apply AI to fiber-optic fence technology or radar.” The feature highlights the growing demand for autonomous perimeter technologies capable of improving situational awareness and preventing intrusions in complex environments where failure is not an option. PureTech Systems continues to support government and critical infrastructure partners by delivering high-accuracy detection and classification that results in near zero nuisance alarms . Our focus remains on autonomous perimeter and border protection —including automated deterrence —designed for high-consequence border and critical infrastructure protection applications. Read the original Wall Street Journal article: https://www.wsj.com/tech/trumps-border-spending-spurs-boom-in-ai-infused-surveillance-4714521b Previous Next