PureActiv wide area and security perimeter protection solutions helps utilities and critical infrastructure improve the safety and security of generation and distribution facilities and assets with a tangible Return on Investment. Utilities Solutions Learn More Concerns over keeping our nation's utilities protected from terrorist attacks have heightened in today's world. Our societies are entirely dependent on the delivery of power and water. Given the importance of this infrastructure to communities worldwide, it is imperative that necessary proactive measures be taken to secure these critical generation and distribution infrastructures from terrorists intending to do harm. PureTech’s PureActiv wide area and perimeter protection solutions help utilities improve the safety and security of power and water generation and distribution facilities, including their assets with a tangible Return on Investment. The PureActiv system secures perimeters by autonomously detecting, alerting, and deterring ground and air intrusion events at the perimeter, before they result in more catastrophic events, loss of life, and operational downtime. PureActiv provides actionable intelligence and situational awareness enabling utility authorities and law enforcement to intervene to deescalate situations and quickly return to normal operations. With decades of successful fielded deployments and continuous innovation, PureTech’s patented geospatial AI-boosted video analytics provides very high probability of detection with a very low nuisance alarm rate only sending near real-time auto-verified alarms to security personnel. Key Benefits: Increased operator productivity by reducing workload assessing nuisance alarms, Lower total acquisition and support costs, Improved site security by automating perimeter detection, deterrence and response, and providing enhanced situational awareness, and Plug and play integration into your existing system. Request PDF Active Threat Detection "We selected PureTech Systems for our perimeter surveillance software solution due to their ability to integrate best-of-breed perimeter sensors including their award-winning video analytics. After working with them for over 7 years, we are extremely pleased with not only the capabilities of their solution, but also the effort they bring to the table to ensure our needs are met. We consider them a valuable partner." - MANAGER OF NUCLEAR SECURITY PLANNING AND PROJECTS, UTILITY IN NUCLEAR INDUSTRY Key Features: Utilizes existing cameras, radars, fence sensors, access control, NVR, network, Eliminates nearly all nuisance alarms using ML, Gives your NVR live geospatial map capability with no integration required, Autonomous PTZ camera lock-on-target tracking keeps intruder in camera view, Location of detected targets show on GIS Map, No operational changes, Easily scaled to fit your budget and needs today and tomorrow, and Longer detection and classification (PurifAI) ranges reduces infrastructure costs by as much as 30%. Low Signal Detection PureTech’s solutions include and combine integrations with other sensor systems to provide the most effective and affordable safety and security solutions. The solutions scale incrementally so that you only pay for the capabilities you need when you need them. Object Left Behind Behavior Recognition: Zone entry/exit, Stopping, Loiter ing, Crowding, Travel path, Object left behind, Crawling, walking, running, Wrong direction of travel, Thrown object, Removed object, Person & vehicle tailgating, and Counting. Learn More Need a Rapid Deploy Solution? (R-DAPSS) enables airports, borders, military bases, seaports, transit agencies, and utilities to quickly deploy a temporary or permanent high fidelity virtual perimeter system at substantially less cost and time than a hard-wired solution. The PureActiv R-DAPSS provides the same best-in-class level of perimeter intrusion detection, auto-verification, and automated deterrence as a hard wired-solution. Security and facility managers can now secure their perimeters quickly without planning and executing a large construction project. Pure Activ ® Rapid-Deploy Autonomous Perimeter Surveillance System (R-DAPSS) Learn More Pure Tech Products PureActiv AI-Boosted Video Analytics Cameras PurifAI PureActiv Sensor Integration Platform PureActiv AlertView C2 System Rapid Deploy Solutions Radars

< Back PurifAI for TMA's Dispatch Spring Edition May 28, 2025 We’re excited to announce that PureTech Systems’ PurifAI® solution is featured in the Spring 2025 edition of TMA Dispatch , the official publication of The Monitoring Association. The ad highlights how PurifAI® empowers central stations with AI-boosted geospatial video analytics — delivering smarter, faster alarm verification from any video alarm source. Fully integrated with platforms like Immix and more, PurifAI® helps reduce nuisance alarms, improve operator efficiency, and elevate monitoring service value. Check out the full issue here: TMA Dispatch Spring 2025 Previous Next

< Back Unlocking Central Monitoring's Potential Nov 14, 2023 Central Monitoring Stations have entered a new era of efficiency, driven by the power of Artificial Intelligence/Machine Learning (AI/ML)- boosted video analytics. ML involves using advanced algorithms designed to improve performance and accuracy through experience and training data. Video analytics systems start with an initial dataset comprising of examples with defined objects, actions, or events to recognize. These labeled examples encompass a wide range of scenarios, from people walking and animals to intruders entering secured areas. This initial training phase serves as the foundation upon which a system's profound understanding of video content takes shape. Once the system is equipped with this foundational knowledge, it utilizes various techniques for feature extraction from video frames, such as object shapes and motion patterns. These features serve as the building blocks for making predictions and identifying objects or actions within the video feed. The system also selects appropriate ML algorithms, such as convolutional neural networks (CNNs) tailored to specific use cases like object detection, tracking, or activity recognition. But here's where the magic truly unfolds: a feedback loop, comparing these predictions with the desired to measure the accuracy of its predictions. In times where discrepancies arise, it learns, adapts, and evolves. This might look like adjusting algorithm parameters, refining feature extraction methods, or incorporating new data. This constant journey of adaptation and perpetual learning leads to improved accuracy and reliability. How Do Video Analytics Really Work? Now, picture a bustling Central Monitoring Station, where video analytics systems take in and process a steady stream of un-labelled video data from diverse sources. Drawing from learned knowledge, they effortlessly provide predictions on the presence of people, vehicles, and specific behaviors within the video feed. By harnessing the power of advanced computer vision algorithms and ML, video analytics distinguish between true security threats and nuisance/false alarms with precision. Once an object of interest is autonomously detected, the auto-verification process of said object begins. Using ML as explained above, the object will be classified and tracked, while simultaneously issuing an alarm for operators to take swift action. With PureTech Systems’ PurifAI, PureTech’s patented and award-winning AI/ML-boosted video analytics seamlessly integrates with your existing infrastructure, eliminating the need for an extensive overhaul. It's as simple as adding PurifAI to the loop to instantly transform your Central Monitoring Station. Previous Next

< Back PureTech Systems Inc. Introduces PurifAI Dec 12, 2024 Phoenix, AZ – PureTech Systems Inc ., a leader in geospatial AI-boosted video analytics for wide-area perimeter and border security, is excited to announce the launch of PurifAI , an award-winning and Patented SaaS solution designed to filter false and nuisance alarms for self and central monitoring operations . The patented cloud-based service receives video motion alarms from any source (new or existing cameras, DVRs, NVRs, other video analytics, motion sensors), and reassess them using PureTech’s cutting-edge AI-boosted video analytics to filter out false/nuisance alarms. Only auto-verified alarms that pass the AI filtering are forwarded to your central monitoring software, and/or SMS, and/or email. False/nuisance alarms are logged, but not forwarded so you don’t have to waste time looking at them. Key Benefits of PurifAI: · Eliminates nearly all false/nuisance alarms, · Exponential Growth - Enables you to monitor dramatically more sites without adding staff. · Reduced Monitoring Costs - With less time spent dealing with false/nuisance alarms, you can simultaneously improve your overall monitoring efficiency and effectiveness. · No Additional Infrastructure Required - PurifAI works seamlessly with your existing video surveillance infrastructure, such as NVRs, DVRs, and cameras; requiring no additional on-premises equipment. · Happy Employees Equals Happy Customers - Operators are more effective, which contributes to improved job satisfaction, resulting in greater employee and customer retention. "We are thrilled to incorporate our award-winning, patented AI-boosted video analytics in our PurifAI SaaS offering. " said Larry Bowe, CEO of PureTech Systems Inc. " Our AI-powered SaaS solution delivers exceptional filtering accuracy, effectively eliminating nuisance alarms. This empowers central monitoring organizations to enhance service reliability, seamlessly scale their operations, and significantly reduce operating costs." PurifAI is now available for subscription. To learn more about this innovative solution and how it can enhance your security operations, www.verifyalarms.com , call 602-424-9842, or email info@puretechsystems.com . Previous Next

< 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

With 17 years of continuous innovation and fielded systems, PureTech Systems is delivering edge computers with PureActiv Inside for high accuracy autonomous perimeter intrusion detection at the edge. Edge Computers with Pure Activ ® Inside Learn More With decades of continuous innovation and fielded systems, PureTech Systems is delivering edge computers with PureActiv Inside for high accuracy autonomous perimeter intrusion detection at the edge. Edge intrusion detection devices can be installed in the field at the edge, rather than hosting the software in environment-controlled equipment rooms that can be expensive to maintain. With less moving parts, the edge device enables users to spend less time dealing with devices and more time on real alarms. The AI-boosted edge devices enables users to add PureTech's best-in-class geospatial AI-boosted Video Analytics to new or existing systems for increased site security and situational awareness. Robust perimeter intrusion detection, including identification, tracking, alerting, and autonomous PTZ following, enabling you to always keep targets in view, Precise target geolocations on a real-time map in your NVR with no integration required, Easily scales to fit your budget and needs today and tomorrow, Eliminates nearly all nuisance alarms, Works with existing cameras, radars, fence sensors, access control, and NVR network, Less network bandwidth by processing at the edge, Lower total cost of ownership; requires less maintenance, and No changes to operations. Key Benefits Request PDF Edge Computers TESTIMONIALS "PureTech Systems Inc. is a Gold Tier Member of Intel’s Partner Alliance program, and we fuel a powerful fusion of advanced hardware for edge computing and award-winning AI-boosted Video Analytics, driving innovation and affordable security. Together, we are revolutionizing security solutions, creating a safer and smarter world for all." Delivering AI-Boosted Video Analytics Capabilities at the Edge Supported Behaviors Zone Entry/Exit, Stopping, Loitering, Crowding, Object Left Behind, Travel Path, PTZ Camera Drone Tracking, Moving too fast or too slow, PTZ Camera Handoff, PTZ Follow specified target, Crawling, Walking, Running, Wrong Direction of travel, Thrown Object, Removed Object, Person & Vehicle Tailgating, and Counting. Drone & Person Detection Auto-Detect and Geolocate Intrusions PureTech Geospatial Video Analytics, Fence Sensors, Radars, Ground, and Other sensors. Auto Classify Intruders Autonomously steer cameras, dispatch drones to gelocation of intrusion. Using ML, classify target as a threat/non-threat. Auto Alert Personnel & Keep Track of Intruders Issue alarms - Mobile devices, Workstations. Autonomously follow intruders. Keep airborne and ground intruders in camera view. Auto Deter & Defeat Blast intruders with deterrent sounds and strobe lights. Jame drone communications and GPS. Outputs to VMS Options: Dynamic map as video output, Alarms via dry contact, Alarms via software integration, Live video with annotations, and Recorded alarm video with annotations. Sample Architecture Diagram Existing or New Fixed, PTZ Cameras or Radars PureActiv Edge Computer Your VMS Call to Order

< Back A Recap of DSEI 2023 Sep 22, 2023 The Defense and Security Equipment International (DSEI) event is a significant gathering of defense and security professionals, showcasing the latest innovations and solutions in the field. For PureTech Systems, attending DSEI presented a unique opportunity to explore emerging trends and engage with like-minded organizations, governments and government consultants. At the event, we witnessed a remarkable level of interest from various companies specializing in border security and critical infrastructure protection. This time around we saw the heightened concerns of European nations regarding Russia and Ukraine, and the relevance of our technology in addressing these issues. At DSEI 2023 that ended last Friday we witnessed an overwhelming interest in our technology from European companies and government consultants in the field of border security and critical infrastructure protection. PureTech’s innovative AI-boosted video analytics solutions and integrated command and control system offer a promising avenue for enhancing operational control and security. PureTech is at the forefront of technological innovation when it comes to border security and critical infrastructure protection. Our security solutions have been successfully deployed in the United States, setting new standards for operational control and threat detection. This technology is highly relevant in the European context, where the need for advanced and field-proven security measures has become increasingly pressing. During DSEI, PureTech leveraged the opportunity to engage in numerous constructive meetings with representatives from various border security agencies and government consultants across Europe. These interactions, along with our existing US-based and European partners, provided valuable insights into the specific challenges faced by European nations in securing their borders and critical infrastructure. Seeing Beyond the Obvious PureTech’s strength lies in its ability to see beyond the obvious. We recognize that securing borders and critical infrastructure demands more than just traditional methods. Our advanced technology empowers security agencies to detect, track, and support operational response to threats proactively. This predictive capability, combined with the efficiency of our integrated command and control system, makes us the solution of choice for those committed to bolstering security in an uncertain world. As we move forward, PureTech is committed to collaborating with its partners to deliver comprehensive security solutions that provide the level of protection necessary in today's complex world. Our 17 years of experience in the field, coupled with dedication to making borders and critical infrastructure secure is unwavering, and we stand ready to meet the evolving needs of our global partners in safeguarding their nations and assets in the US, Europe, Middle East and worldwide. Previous Next

< Back PureTech Integrates with Intel's OpenVINO! Mar 24, 2023 PHOENIX, Ariz. – PureTech Systems has announced its integration with Intel's OpenVINO to deliver its’ AI-boosted video analytics on edge devices with lower cost, lower power consumption, and at a smaller size. OpenVINO is a powerful toolkit developed by Intel, which allows developers to deploy their computer vision applications on a variety of hardware platforms, including edge devices. By integrating with Intel, PureTech Systems' AI-boosted video analytics can now run on edge devices without the need for a discreet GPU card, which are expensive and power hungry. "Through our partnership with Intel, we are able to make our technology more accessible and affordable for a wider range of customers," said Larry Bowe, President of PureTech Systems. "This is a significant step forward in our mission to deliver the best possible solutions for our customers." This new integration emphasizes the company's commitment to delivering cutting-edge technology. By leveraging its’ AI-boosted video analytics capabilities, PureTech Systems is well-positioned to drive technological innovation for critical infrastructure protection, and to create long-term value for its customers. To learn more about this technology, PureTech Systems will be at the ISC West conference next week in Las Vegas, NV. Come by booth 3055! Previous Next

PureTech Systems provides high accuracy automated perimeter detection systems with near-zero nuisance alarm with their patented geospatial AI-boosted video analytics and perimter intrustion detection sensor integration software, PureActiv. AUTOMATED PERIMETER INTRUSION DETECTION SYSTEMS Who is Pure Tech? PureTech is transforming perimeter security with advanced automation software to protect facilities, critical infrastructure, and borders. We save lives, prevent theft, and reduce operational disruptions while significantly lowering security costs by minimizing manpower needs—all while delivering a stronger, more effective security solution. Markets We Serve Airports PureActiv's automated perimeter protection, with near-zero nuisance alarms empowers transportation authorities to significantly enhance the safety and security of airports. Read More Borders PureActiv AlertView Common Operating Picture uses GIS map-based technology, providing users with high situational awareness for effective interdiction. Read More Military Bases PureActiv detects perimeter intrusions early, allowing military authorities and law enforcement to swiftly intervene, deescalate situations, and restore normal operations, with near-zero nuisance alarms. Read More Rapid Transit PureTech’s PureActiv helps transportation authorities improve the safety and security of the rail operations with a tangible Return on Investment, and near-zero Read More Seaports PureActiv delivers automated detection, verification, alerting, and proactive deterrence of potential safety and security threats with near-zero nuisance alarms. Read More Utilities PureActiv provides actionable intelligence and situational awareness with near-zero nuisance alarms, enabling security professionals to concentrate on only verified security events. Read More Video Central Monitoring Powered by PureActiv AI-boosted video analytics, this SaaS delivers central monitoring stations with only verified alarms, nearly eliminating nuisance alarms. Read More Why Pure Tech? Greater accuracy – Higher probabilities of detection and near-zero false alarm rates using Deep Learning. Autonomous PTZ and fixed camera lock-on-target tracking. Automated geo-location of detected objects. Auto-verification (person, car, truck, etc.) of intrusion detections from Video, Radar, Fence Sensors; virtually eliminates nuisance alarms. Behavior Identification – entry, exit, loitering, stopped, object left-behind, counting, crowding, travel path, person and vehicle tailgating, crawling, running, walking, and much more. Short to long range ( up to 5 miles) detect, classify, and track. AI Analytics processing on lower resolution up though Megapixel cameras and on a range of thermal and visible imagery. Supports Industry Standard sensor and external system interfaces. Integrates with automated deterrents; loud hailers, lights, and other deterrent sonic devices. Cost effective, flexible & scalable; deployed via edge devices or servers. Schedule a Demo Pure Tech Integrated Automated Products Cameras Our field-proven geospatial AI-boosted video analytics with near-zero nuisance alarms available on short, medium, and long-range cameras. Read More PureActiv Sensor Integration Platform PureActiv integrates with other perimeter sensors such as radar, buried or on-fence fiber detection systems, and ground sensors either singularly or combining them to provide just the right level of protection to cost effectively meet your perimeter requirements. Read More Edge Computers with PureActiv Inside Small, cost effective computers equipped with PureTech's patented, geospatial AI-boosted video analytics at the edge. Read More PurifAI A monthly SaaS, hosted on verifyalarms.com , that uses AI Deep Learning enhanced video analytics to eliminate false/nuisance alarms Read More PureActiv AI-Boosted Video Analytics PureTech's patented geospatial AI-boosted video analytics provide users with the best-in-class probability of detection with the lowest nuisance alarm rates Read More Radars Short, medium and long-range ground and aerial radar technology paired with our geospatial AI-Boosted Video Analytics to offer a layered surveillance solution. Read More PureActiv AlertView Command & Control PureActiv AlertView provides security professionals with accurate and reliable real-time alarms and video of suspicious activity in outdoor and remote environments while minimizing nuisance alarms. Read More Relocatable, Rapid Deploy Solutions Short and long-range surveillance solutions equipped with a suite of radar and camera sensors mounted on a relocatable, rapid deploy device. Read More Testimonials “I have relied heavily on PureActiv and thrived off its benefits. PureActiv is a gamechanger , no doubt about it”. - FEDERAL GOVERNMENT USER Pure Tech Applications Counter Drone PureActiv autonomously detects, classifies, and tracks aerial threats, while providing real-time alerts and visual confirmation on a innovative geographic map with no integration required. Read More False Alarm Elimination PureTech’s patented DL Auto-Verify provides relief to users experiencing excessive nuisance alarms caused by radars, environmental factors, and fence sensors. Read More Rail Intrusion Detection PureActiv automates the real-time detection and notification of any object of concern on the rail tracks so risks are mitigated with minimal operational impact. Read More Tailgating & Piggybacking PureActiv safeguards access points to your secure facilities against unauthorized entry, ensuring comprehensive perimeter intrusion detection. Read More Mobile Surveillance The same best-in-class level of perimeter intrusion detection, auto-verification, and automated deterrence as a hard wired-solution, available on a mobile platform. Read More Perimeter Intrusion Detection PureActiv secures perimeters by autonomously detecting, alerting, and deterring ground and air intrusion events at the perimeter, before they result in more catastrophic events. Read More News & Upcoming Events Schedule a Demo News Discover the most recent developments and announcements from PureTech Systems. Read More Events Stay connected and engage with PureTech Systems at upcoming industry events. Read More

Smart Cities Solutions Schedule a Demo By 2050, it is estimated 60% of the world's population will live in cities (source TechRepublic.com). Smart Cities use intelligent video and other sensors to detect the movement of people and vehicles and issues affecting cities such as vehicle accidents, traffic jams, overflowing garbage bins, streetlight outages, and road quality issues. The data from these sensors can be used to improve the lives of citizens and visitors and ensure sustainable cities. Optimizing traffic flow of people and vehicles including quickly finding parking ensures a great experience and minimizes air pollution. Street and store traffic statistics can provide building owners with information to properly price their lease spaces. Emergency responders can leverage the real-time and historical information to ensure the appropriate response to security and safety events. PureTech’s AI video analytics can be used to address many of the detection needs of smart cities. Key Automated Detection Capabilities: Cloud and edge-based deployments. Presence of vehicles to control intersection lights. Crosswalk incursion to alert pedestrians and drivers. Driver infractions, such is illegal turns or failure to yield. Vehicles blocking intersections. Wrong direction of travel. Vehicle counting for traffic statistics. People counting for building owners to justify leasing rates. Street and lot parking stalls availability. Parked vehicle duration. Overflowing garbage bins. Dark streets or alleys indicating lighting outage. People and vehicles in unexpected locations at unexpected times. Potential unsafe overcrowding. Person loitering. Person down (lying on the ground). Schedule a Demo Pure Tech Products

PureActiv AI Deep Learning Geospatial Video Analytics integrates with other perimeter sensors such as radar, buried or on-fence fiber detection systems, and ground penetrating radar either singularly or combining them to provide just the right level of protection to cost effectively meet your perimeter requirement. PureActiv® Sensor Integration Platform Learn More Many times, the right detection solution is not a single sensor technology. In addition to its geospatial AI-boosted Video Analytics , PureActiv integrates with other perimeter sensors such as radar, buried or on-fence fiber detection systems, and ground sensors either singularly or combining them to provide just the right level of protection to cost effectively meet your perimeter requirements. Additionally, given the extensible open-architecture approach, additional sensors can be added when needed or budgets permit. The right sensors are easily integrated to ensure a perfect fit to your needs so your firm is not wasting money on too much or too little capability. Key Features & Benefits Rapid open architecture integration of detection sensors and sensor systems. Integration into Genetec, Milestone, or PureActiv user interfaces . Supports short and long range radars including, but not limited to, Magos, SpotterRF, Echodyne, Elta, Fortem, FLIR, SR Hawk, and SSR Tracker. Supports various fence and buried fiber detection systems including Optasense, Southwest Microwave, Senstar, and others upon request. GPS integration for friend (blue force)/foe tracking. Integration of AIS ship transponder systems for ship tracking. Automatic drone dispatch – dispatch drone to alarm location and stream live video. Person and vehicle tailgating detection. Many other sensor and systems integrated to help seal your perimeters. Automatic slewing of PTZ cameras to intrusion locations. PurifAI - only alarms on AI verified targets of interest eliminating nearly all nuisance alarms. PTZ Auto-follow – autonomously follow intruders with PTZ cameras. Automatic non-lethal deterrents including LRAD, audio alarms, and/or laser dazzlers to scare away intruders. Track fusing from multiple radars and PureActiv AI Video Analytics, reduces multiple alarms for the same intrusion event. Geospatial zones and zone logic to reduce multiple alarms from the same event. TESTIMONIALS “The integration of PureActiv with our Magos radars is the best we’ve seen and the PureTech team did it without needing any technical support from Magos!” - YARON ZUSSMAN, GENERAL MANAGER, MAGOS AMERICA Effortless Integrations With Request PDF Technology Partners

Mobile Security Surveillance, tailored blend of fixed, mobile and portable security surveillance systems and Our Rapid Deploy Virtual Perimeter System enables airports, country borders, military bases, seaports, and utilities to quickly deploy a temporary or permanent high fidelity virtual fence. Relocatable, Rapid Deploy Solutions Rapid-Deploy Autonomous Perimeter Surveillance System (R-DAPSS) The PureActiv R-DAPSS provides the same best-in-class level of perimeter intrusion detection, auto-verification, and automated deterrence as a hard wired-solution. Learn More Mobile Surveillance System Along the United States border, Mobile Surveillance systems provide long-range mobile surveillance and consist of a suite of radar and camera sensors mounted on standard pickup trucks. Learn More TESTIMONIALS "We use PureTech Systems video analytics to secure the perimeter around our critical manufacturing and test campus long with general video surveillance for our other sites across the nation. We monitor all these sites from a single command center. PureTech's technology delivers on its commitments, and they can be counted on to provide world class customer service." - SECURITY OPERATIONS MANAGER FOR A U.S. GOVERNMENT MANUFACTURER