Yair Siegel, CEVA
embedded.com (January 03, 2017)
Video analytics are used to examine objects, people, situations, and more, and to then generate conclusions using deep learning and computer vision algorithms. Many implementations send footage over the cloud for processing, but is that such a good idea? The cloud is a useful and powerful resource for storing and processing data, but -- as video cameras become more ubiquitous and more intelligent -- the question of whether video analytics should be performed locally or remotely must be addressed. Issues of privacy, safety, security, and cost are strong grounds for the use of edge processing, meaning performing the video analysis onsite (i.e., edge analytics). With CES 2017 starting in just a couple of days at the time of this writing, a variety of cutting-edge prototypes will be showcased -- what trend will take the crown?
Every fraction of a second counts in ADAS
One of the most pervasive uses of video analytics is in the automotive industry for advanced driver assistance systems (ADAS) in highly-automated vehicles (HAVs). The vision systems on HAVs use multiple cameras to identify traffic signals, vehicles, pedestrians, and other indicators, and then respond accordingly. This requires split-second response times and any delay is intolerable. So, relying on a remote server to process the data is not an option. Even if the communication speed is theoretically adequate, the data from each camera covering every angle of the vehicle, together with the growing number of camera-equipped vehicles, could stress available bandwidth, thereby causing an unacceptable delay.
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