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FCW : May 15, 2014
tion and video analytics to detect anomalies in a child’s eye contact with adults that could indicate autism. The automated approach eliminates hours of studying video frames to identify moments of eye contact, researchers said. Video analytics technology is also finding a niche in humanities research. The Large-Scale Video Analytics (LSVA) research effort uses supercomputing power to explore video collections. The project brings together researchers from the University of Southern California (USC); the Institute for Computing in Humanities, Arts and Social Sciences; and the National Center for Supercomput- ing Applications, which is supported by grants from NSF and other federal agencies. Virginia Kuhn, an associate professor in USC’s School of Cinematic Arts, said it is hard to imagine an area in humanities research that would not benefit from improved video analytics. “There [are] the 115 years of cinema [and] the massive amount of broadcast television and cable shows that are migrating to sites like Netflix, Amazon [Instant] Video and Hulu,” she said. “All of these are important aspects of cul- ture insofar as they impact our sense of identity and our knowledge of the world.” The fundamentals Video analytics software that zeros in on the object or event of interest is part of a broader architecture that includes cameras, encoders, servers, storage and networks. The ana- lytics capability might reside on servers, the cameras or the encoders, which convert video from analog cameras so the moving images can travel over IP networks. Moving analytics to the edge — on cameras or video encoders — provides several advantages, according to Scott Dunn, director of business development at Axis Commu- nications, a network video vendor. “First, the camera can process all the video before it’s sent over the network,” he said. “So, for example, instead of streaming constant video to read license plates, the cam- era knows to send only the relevant five-second clip and process the plate number. This also means the video being analyzed is raw and uncompressed, as opposed to server- based intelligence that uses compressed video. The end result of edge processing is that video no longer must be sent to the centralized server, meaning you can dramati- cally decrease bandwidth and centralized computing power needs, increasing savings for the total system.” Indeed, storage is an important, if unsung, component May 15, 2014 FCW.COM 29 Video analytics are most commonly used for law enforcement and intelligence, but there are other missions as well. This National Science Foundation-funded project tracks eye movement to screen for autism. The automated process eliminates hours of video review. It’s not just security NEWS.GATECH.EDU
April 30, 2014
May 30, 2014