In the outdoors, trying to accurately detect perimeter intrusions over large areas can be like looking for a needle in a haystack. Moving foliage, wind, weather, even ripples in water can trigger nuisance alarms.

GPS positioning data plays a vital role in reducing perimeter security nuisance alarms.The key is to select intelligent videos cameras that have been designed from the ground up to be inherently “geo-registered.” This means that the camera’s field of view (FOV) maps the GPS coordinates of all points in the landscape under surveillance. Such an approach is fundamental to achieving high performance and accuracy in an intelligent video surveillance system.

For indoor surveillance where distances are small, geo-registration is not as important. But think about the outdoors, where blowing trash, moving leaves or small animals will constantly cross the camera’s view. In these cases, knowing an object’s actual size becomes more important.

That’s because cameras lack depth perception. To an outdoor camera, a small object that’s close to the camera will appear substantially larger than a person standing off in the distance. Without GPS details, the camera will likely ignore the human in the distance and send alarms for the closer objects. As a result, the system will generate an overwhelming number of nuisance alarms and quickly lose all accountability.

On the other hand, when an intelligent video camera uses GPS location information to monitor a scene, the camera can make very accurate decisions based on the size of all detected objects. GPS-based analytics allows the camera to filter the small animal while still detecting the human-sized intruder in the distance. When such cameras are designed with sufficient image processing to clean up the other outdoor issues (motion from wind, changing lighting, clutter from foliage, and weather), the result is comprehensive security.

Geo-registration, which can also be used to automatically steer PTZ cameras to track and follow targets, also has a direct impact on lowering project costs while leading to an installation that’s on time and on budget. We’ll return to this important topic in the future, so subscribe to the blog and never miss an update.

In our continuing series for reducing perimeter security nuisance alarms we’ve addressed the benefit of image processing and the value of electronic image stabilization. Another requirement for accurate perimeter intrusion detection is for an automated camera to “see” security violations against a background of movement and clutter. Practically speaking, this means the camera must be smart enough to ignore movement from “unimportant” objects. Such movement is also known as non-salient motion.

By definition, non-salient motion comes from objects that always return to the same position within a brief period of time, like branches, trees, foliage, or reflections in water. Alternatively, salient or “important” motion comes from objects that traverse the camera’s field of view, like a person or vehicle.

From a security perspective, salient motion is what matters. It’s activity that likely represents a legitimate intruder entering a secure area which requires investigation. On the other hand, non-salient motion is normal behavior in the outdoors, and must be filtered by the camera or the nuisance alarm rate will reach a level so high they will likely be ignored.

For indoor surveillance applications, such distinctions are irrelevant because background objects are not moving. In the dynamic outdoors the differences between these two types of motion is more important. Think of a typical outdoor scene: a large tree will sway in the breeze within the same general location. The same thing happens with leaves, bushes or reflections in water. Obviously, you want such movement to be ignored, or risk wasting your security efforts chasing after phantom issues.

Filtering such movement is best accomplished with sufficient on-board processing and video memory buffers for making the right determination. This is no easy task, especially considering the amount of data that a camera needs to analyze over a large outdoor scene spanning hundreds of meters. Importantly, when we refer to “video memory buffers” we’re not talking about on-board video storage for future retrieval. In this case, we’re talking about very high-speed memory that’s used for real-time scene analysis.

For example, a large tree takes a period of time to move in one direction and then just as long to return. Cameras that lack sufficient memory will only see the tree moving one way and trigger an alert. On the other hand, if the camera can analyze and store scene information over a longer period of time it can conclude that such background movement returns to the same area and is an “unimportant” object to be ignored.

You can see a real-world example of an intelligent video surveillance camera accurately determining salient from non-salient motion in the following video. Notice how the moving brush and foliage on the left is safely ignored, along with the heat waves coming off the vehicle, while the human targets — which are actually smaller than some of the background objects that are ignored — are accurately detected and alarmed. In fact, this video also shows how intelligent video cameras can “see” what the human eye may have missed.

Without sufficient on-board processing and video analysis memory to filter non-salient motion, such an environment would most likely be overwhelmed with nuisance alerts.

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Camera movement can be an obstacle to video detection accuracy in the outdoors, and a big source of nuisance alarms. That’s because it’s difficult for automated cameras to detect movement in a scene when the whole field of view is also moving. For indoor surveillance applications, such camera shake is rarely a problem. In the outdoors, where cameras are mounted high on poles, even a slight wind or vibration can cause nuisance alarms.

Of course, wind and vibrations are rarely slight in the outdoors. Many video intrusion detection systems are deployed along open areas that are difficult to patrol — railways or railyards,  airport perimeters, national borders, seaports facing open water, among others — areas that are naturally impacted by high winds or vibrations from planes, trains, weather and machinery. Without effective image stabilization, these applications can be overwhelmed by nuisance alarms or worse, outright misdetects.

The best way to overcome the impact from wind or vibrations is to first stabilize the image electronically, before the video analytics take place.  Our last article in our nuisance alarm series looked at the value of image processing as a foundational step for accurate outdoor surveillance. With such processing cameras can first electronically stabilize the image for translation/rotation and zoom effects, sometimes refered to 3D stabilization, before the video analysis takes place.

You can see this in action with the following video, taken with an automated thermal detection camera. On the left, you’ll see the raw video as it enters the  security camera and the effect of wind and motion. On the right, you see the image after stabilization with the video analytics detection. Notice how the camera is still able to detect the second intruder in the distance without triggering nuisance alerts.

Importantly, electronic stabilization is most effective when integrated directly on-board a surveillance camera, and ideally as a dedicated processor.

Our next post in this continuing series will address the impact that small movement and clutter has on accurate outdoor detection. Subscribe to the SightLogix blog and keep updated on this topic as well as other articles related to outdoor video security.

Most security folks would agree that deploying reliable automated video systems in the outdoors has been challenging, at best. Generally speaking, video analytics have worked well in controlled environments, such as those in the static indoors. Outdoor security applications are a different story.

Challenges notwithstanding, it is possible to deploy intelligent video surveillance cameras in the outdoors that maintain a high probability of target detection while also addressing the core issue of nuisance alarms. The critical first step  is to focus on image processing in advance of the video content analysis. Such processing resources – often encompassing several digital signal processors (DSPs) –make it possible to analyze the full visual detail of every video frame — inside the camera, at the network edge. In this way, intelligent cameras have the horsepower to clean up the problems associated with the outdoors.

This includes electronically stabilizing the image for camera motion, adapting to changing lighting, fog, rain, snow and sandstorms, and filtering variables such as small animals, blowing debris, trees moving in the breeze and reflections from water.

With such complexity in the outdoors it’s not possible for automated video cameras to accurately determine legitimate targets unless they bring a high degree of image processing to the network edge. When video processing and analysis is performed by video encoders separate from the camera — or by servers in the datacenter — they perform their analysis on a small fraction of the available scene information, at times less than one percent due to preparing data for transmission. Analyzing 100% of the raw scene data directly in the camera as it leaves the imager greatly improves the probability that cameras will accurately detect targets and filter the outdoor impediments that would otherwise trigger nuisance alarms.

Without on-board image processing of sufficient power, the only way to prevent excessive nuisance alarms outdoors is to lower the sensitivity of the system, directly impacting camera range and detection accuracy.

There’s been great disappointment among customers in cases where video intrusion detection systems have been deployed that are not designed to address the outdoor challenges. Substantial on-board image processing in advance of the video analysis is a foundational step, upon which a range of capabilities can be built. These include georegistration of targets, dynamic lighting correction, electronic image stabilization, automatic PTZ steering, and other important security functions. Greater image processing also translates into cost savings through the extended range and such processing affords.

We’ll discuss these additional technologies in subsequent articles. Subscribe to the SightLogix blog and keep updated when they’re published.

When properly deployed, video analytics boost security by automatically alerting personnel to take action when an event occurs, freeing them from watching an increasing number of video displays. Intelligent video leverages the inherent strengths of machines and people. Automated sensors never tire, can cover large distances, and “see” what the eye would miss, even in the absolute darkness. People can then make smart decisions based on good information when actual violations occur. And for many indoor surveillance applications, this is often the case.

The key to eliminating nuisance alarms outdoors is to use technology properly designed for outdoor applications rather than misapplying analytics intended for more controlled indoor surroundings

When it comes to outdoor surveillance, too often the reality is different. Outdoors, the security officer who was supposed to be more efficient now spends his time dealing with nuisance alarms that result when a gust of wind or a change in lighting triggers the video detection system inappropriately. As a result, security personnel come to distrust the system, and may tune down the detection sensitivity or possibly even turn off the alarms themselves.

Some facilities have hundreds of nuisance alarms every week. One reason is that video analytics are often being used outdoors in applications for which they weren’t designed. The time security personnel spend addressing these nuisance alarms can negate the claimed efficiency advantages of intelligent video, while redirecting their efforts away from other areas of security importance.

The key to eliminating nuisance alarms outdoors is to use technology designed for outdoor applications rather than misapplying analytics intended for more controlled indoor surroundings. Indoors, a camera only needs to see a limited field of view in typically controlled surroundings. It’s a mistake to apply the same technologies to monitor critical infrastructure applications such as transportation, energy, utilities or large campuses in the outdoors where conditions are continually changing.

Using intelligent video to secure large outdoor venues requires the use of specific technologies like sufficient on-board camera processing power to overcome lighting and weather issues and accurately detect and track legitimate targets from extraneous surrounding motion and clutter. Such systems can also employ geographic information system (GIS) coordinates to determine a target’s location, size and velocity,even over large fields of view.

Although outdoor video surveillance applications are challenging, it is possible to deploy automated outdoor systems to deliver high-accuracy detection of targets over a large area while greatly reducing nuisance alarms.

This is the first post of a series that will explore this important topic in greater detail. Subscribe to the SightLogix blog and keep updated on future articles.