Loitering with Intent – Detecting Suspicious Behavior using Video Analytics

(An earlier version of this article first appeared in the City Security Magazine.)

In the security world, one of the primary objectives is to catch the bad guys and luckily, many sensors and gadgets exist to help achieve that goal.  There are fences that can alert, beam breakers that can be tripped, video that can detect, radar that can monitor, proximity sensors that can sense, door and window contacts that can alarm.  Video Analytics LoiteringThese sensors provide annunciation when an event has occurred.  However, what happens when the perpetrator is not yet a perpetrator, but rather a suspicious target?  They haven’t breached a fence, or opened a door, or entered the property.  In many cases, that person can be considered a loiterer, a type of activity that can be detected and provided to security personnel as alerts.     

Loitering is the act of remaining in a particular public place for a protracted time without an apparent purpose.  Laws that try to prevent loitering have been put in place, but in most cases these laws don’t stick, as the courts generally rule that they are unacceptably vague and do not give citizens clear guidelines as to what is unacceptable conduct.  Although not necessarily a violation, loitering is of considerable interest to security personnel.  Loitering often indicates imminent intrusion.  It may also be an act of information gathering for a planned future event.   Gang activity, especially violent crime and drug trafficking often manifests itself in the form of loitering.  Loitering itself may provide advanced warning to an event happening elsewhere, as it is often used as a distraction tactic.

Detected Loitering along a fence

Loitering detection is typically achieved using a camera equipped with video analytics.

There are several sensors that have the ability to detect loitering including radars, in ground sensors and LIDAR, but the sensors typically associated in identifying loitering are security cameras equipped with video analytics.   Today’s video analytics include the ability to track unique targets, and in doing so, they can determine that a specific person, or vehicle, has remained in an area for a period of time that seems suspicious.  This also means these algorithms are not misled by multiple targets that are just passing through the scene.

Each facility and industry may have a different definition of what entails “loitering,” as such, loitering algorithms typically provide a set of parameters that can be adjusted based on the scene or the specific needs of a particular customer.  These parameters include items such as:

Minimum Targets – For many applications a single loitering person can be grounds for an alert.  However, in some settings, such as banks, loitering becomes more of an interest when it exceeds a maximum number of people in a specific region.

Minimum Loiter Time – depending on the facility, or even the view of the camera, the amount of time that a target should remain in an area before it is considered “loitering,” can vary a great deal.

Applying Loitering Video Analytics to specific regions

Loitering can be applied to the entire scene, or to unique areas within the scene.


Region of Interest – Loitering algorithms typically have the ability to be applied to the entire scene, or just a portion of the scene.  Different regions of interest may also have different loitering parameters.

Target Type – Loitering can be specific to the type of target.  In most cases, loitering is thought to be associated with people, but it can also apply to vehicles, ATVs, watercraft and even wildlife.  Many loitering algorithms have the ability to monitor based on the class of target that is of interest.

Real Speed / Resting times– When applying loitering to a large scene with many targets, the act of loitering may better manifest itself as those objects that are, on average, moving at a slower speed, or coming to rest at a greater frequency.

Video Analytics - Loitering, Museum, Point of Sale

Loitering algorithms may also be used to measure points of interest or effectiveness of retail displays.

It should be noted, that loitering is not always a forbearer of bad news as it can also be used to identify positive situations.  Loitering detection is often used for marketing purposes, determining the effectiveness of a retail display or digital signage.  Car lots may use these types of algorithms to see which vehicles are most popular.  Museums may use it to measure the interest in specific exhibits.

The loitering algorithm is also the basis for other more specific monitoring algorithms, including the monitoring of queue length, whereby an alarm is issued when the queue or line length exceeds a length defined by the operator, and crowd detection, which may ignore the duration a target has been in the area and instead be more concerned with the number of objects present, alerting when a threshold has been exceed or when a rapid size change occurs.

Although loitering does not typically entail an actual intrusion or violation of a policy, it is a valuable video detection algorithm that can give the security provider, or marketing person, insight into potential events that may occur or regions that are drawing a higher level of interest.


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