Smart video ensures situational awareness

Smart video ensures situational awareness

Video content analysis assists users when monitoring different environments. This feature highlights the pros and cons of intelligence in the front and back end.

Two years ago, ObjectVideo waged a series of legal battles with some of the most prominent names in video surveillance, for the infringement of intellectual property relating to video analytics; among the defendants were Bosch, Sony, Samsung, and Pelco to name a few. These lawsuits shook the security industry and gave rise to an influx of video surveillance companies who quickly entered into patent licensing agreements with ObjectVideo, in fear of becoming its next target. According to previous reports, Raul Fernandez, CEO of ObjectVideo had not expected the lawsuits to halt the developments of video analytics or to suppress the adoption of the technology. Ironically, no major breakthroughs in the technology have emerged since the whole fiasco. Now, video content analysis (VCA) seems to be stuck in a peculiar situation where the technology is maturing, but with nothing out of the ordinary coming from it. However, its more advanced features are now able to reduce the rates and incidences of false alarms and are better managed in different devices. The industry is becoming more realistic and “down-to-earth” about the capabilities of VCA, instead of chasing after and/or promoting “new and cool” features that will most likely be unreliable once they are put to the test.

The limited breakthrough in the technology however, will not be putting a stop to its growth. According to MarketsandMarkets, the video analytics market is expected to grow from US$180 million in 2011 to $867.8 million by 2017, at an estimated CAGR of 30.4% from 2012 to 2017.

Current forms of VCA are available in front-end (edge-based) or back-end devices. Pros and cons exist for both the front and back end, and each have its most suitable use scenario. According to Mahesh Saptharishi, President & CTO of Video IQ, there are two primary factors that influence the performance of any VCA system – the quality and resolution of the video being analyzed and the amount of processing available to run VCA algorithms. Lower resolution video, such as video with considerable noise and heavily compressed streams all adversely affect performance. Sophisticated VCA algorithms require a substantial amount of processing to run effectively. More recently, advances in the science of pattern-based video analysis have helped dramatically increase performance while requiring a significantly lower processing budget.


Front-End Devices
The advantages of using front end devices are most apparent in two ways – it saves bandwidth and is cost effective. “If there is a large number of cameras requiring analytics, placing analytics on the edge makes sense. Edge placements keep the costs down by relieving network traffic burden, actual costs of the analytics, and costs involved in server farms,” stated John Sepassi, Account Executive at IntelliVision. The use of front-end devices works exceptionally well for remote locations or extended facilities where there is poor or non-existent bandwidth back to the monitoring location, “as it eliminates maintaining a remote server and needs only a low bandwidth connection to send snapshots of alarm instances,” said Sadiye Guler, Founder President of intuVision.


“When video analytics are placed inside a camera, and a high degree of video processing is used to analyze the raw video as it comes off the imager, every video frame at full resolution is available for the video content analysis,” commented John Romanowich, CEO of SightLogix. The ability to analyze every video frame makes VCA on the edge more sensitive to feint objects. “Given the same algorithm, edge analytics using the original data works better than server-based analytics, which works on compressed data from an IP camera. This is because video compression is ‘lossy' and feint objects such as a person in dark clothing on a dark night may just be visible in the original video but filtered out and lost during transmission of compressed video to the server,” said Geoff Thiel, CEO of VCA Technology. Having the loads divided among multiple devices relieves network traffic burden as analytics are performed at the camera level, and only relevant video is sent back to the recorder for storage.

Major disadvantages of using VCA at the front end include its inability to run analytics that require high CPU, but also the analytic configuration that is needed for each device. “Low processing resources result in lower performance and less features, management of VCA on large numbers of cameras becomes difficult, maintenance is hard because each new bug fix or feature requires a firmware upgrade, and new features may not be supported by existing cameras because of the higher processing requirements,” stated Zvika Ashani, CTO of Agent Video Intelligence. Users also have to be sure their VMS is compatible and supports all the features and functions of the VCA in their cameras for it to operate at its full capacity.

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