Aug 1, 2009 12:00 PM, By Leonhard Korowajczuk and Jasper Bruinzeel

Analytics technology is providing public-safety agencies with new ways of using surveillance video

Select image to enlarge.

The surveillance industry has changed dramatically in recent years. Not that long ago, analog camera systems with analog video-tape recording systems were the industry standard. Then, digital video recorder-based solutions were introduced, allowing the connection of a limited number of analog and/or digital camera feeds, using a proprietary hardware platform. Now, surveillance solutions are managed through so-called network video recorders that feature video management software applications operating on standards-based computer hardware.

IP cameras are connected to the NVR server through IP-based networks. Legacy (analog) cameras can be tied into these digital surveillance systems using encoders. The number of cameras within a single system has virtually no boundaries, and complex, distributed architectures can be supported with multiple server and storage locations. As long as a high-speed connection is available, any networked computer then has access through the NVR servers to any of the cameras for video monitoring and system management purposes.

In parallel, broadband wireless technologies have matured, while the allocations of the 4.9 GHz band for public-safety applications fueled the adoption of citywide surveillance systems to address safety and crime effectively in medium to large cities throughout the United States. Since the wireless platforms today are natively IP based, these implementations logically are digital and NVR-based implementations. Typically, cities also look toward integrating existing, legacy systems into the NVR-based solution, such as in-building security cameras systems.

As cities start building their unified, digital surveillance systems and add more and more cameras, one might wonder how this growing number of cameras can be managed. After the change from analog systems to open standards-based scalable, digital solutions, the next paradigm shift within the surveillance industry very well might be the shift to intelligent surveillance, rather than current evolutionary progress, such as the emergence of H.264 encoding and megapixel cameras.

Research has indicated repeatedly that the concentration span of surveillance system monitoring personnel is limited. As the number of video feeds grows, the ability of monitoring personnel to effectively identify suspicious behavior declines. The purpose of video analytics now is to make surveillance systems “intelligent,” but only to a certain extent. There should not be an expectation that the human operator can be replaced, nor that all alarms generated from the video analytics engine can be considered 100% accurate. But the idea is that such systems analyze the video feeds and alert the operator of situations that would require a closer look; a human assessment. The operator now concentrates primarily on only those few camera feeds it is alerted to by the video analytics engine, and the overload of the operator is no longer an issue.

The video analytics process is highly complex and processing intensive. This is why basic video management solutions have not integrated any analytics features beyond basic motion-detection. Specialized video analytics packages are available as overlay implementations to augment the basic video management solutions.

Video analytics engines follow similar steps in analyzing video. First, in the process of segmentation — based on image changes from frame to frame — the video analytics engine identifies the foreground and background pixels. The result of the segmentation process is the identification of a certain number of “blobs,” with a blob being a collection of connected pixels.

Next, the process of classification assigns a class to each identified blob. Given a variety of applications, classes might include people, certain types of vehicles, animals and certain static objects.

As classified blobs move through the field of view, in multiple frames, tracking algorithms are used to follow each blob. By analyzing the class of the blobs in combination with the tracked movement pattern, activity recognition can be derived to identify the behavior of the blobs, and more importantly, possible suspicious behavior. If multiple people blobs converge, we can conclude that a crowd is forming. If a vehicle blob stops moving in an area where parking is prohibited, this vehicle is illegally parked. Typical behaviors that can be identified with analytics engines include crowd forming, object removed/left behind, crossing of perimeter lines and loitering.

Several years ago, due to processor intensiveness, a central video analytics server would be able to handle only very few camera feeds in terms of analytics, making solutions expensive and impractical to implement. As a result, the concept of distributed analytics processing at the “edge” (i.e., with the camera unit) became popular to allow for better scalability. However, edge-based analytics processing introduces the issue of compatibility. For new deployments, only certain encoders or IP cameras that allow for running a certain analytics solution can be used. Furthermore, for existing camera systems, significant upgrades might be required to enable the edge processing.

Based on a review of current video analytics solutions, given the strong advances in hardware processing power, the central processor approach today supports many more cameras per server, compared just a few years ago. But strong variance between solutions exists, with a single server able to support anywhere from 12 behaviors in total to as many as 100 cameras with any number of behaviors.

Want to use this article? Click here for options!
© 2012 Penton Media, Inc.