Today every nook and corner of public place is under surveillance camera. They work round the clock and for 365 days. Thus huge amount of video data is generated. Monitoring a video for more than 20 minutes is tedious to human beings and security officers mostly fail to detect abnormal activities. Thus there is a requirement for “machine assistance” to security officers. Watching video via Internet has become a norm. Popular video servers like YouTube, Dailymotion, and Metacafe provide video free of charge. Netflix is subscription based video server. Sixty hours of video are uploaded every minute in YouTube video servers alone [1]. Thus one can imagine the quantum of video content available in Internet. In the years to come more than half of Internet traffic will be due to video. We encounter problem choosing 'right' or cherry-pick the video from the huge pile of video scattered in Internet. Here to we require some form of “machine assistance” to ordinary viewer.
Video is made up of consecutive sequences of frames or images. Each image contain large amount of pixel information. Images offer very little prior structure to work with [2]. Earlier video databases were small and manual annotation was a possible solution. Today it is not a feasible solution. Present day computing power will manage huge size of data. But automatic analysis of video requires artificial intelligence. Video analytics is the baby step in that direction. Video analytics deals with extraction of information from video with the aid of machine assistance. Video processing means performing some image processing (like resampling or colour correction) on the video content. Most of the time extracted information is overlaid on the video for better human interpretation. Big data analytics is latest buzz word in technical world. Video analytics is considered as a subset of big data analytics.
