Video analytics, also known as video content analysis (VCA), involves a variety of techniques to monitor, analyze, and extract meaningful information from video streams. Although video analytics is still in its infancy compared to other types of data mining, various techniques have already been developed for processing real-time as well as pre-recorded videos. The increasing prevalence of closed-circuit television (CCTV) cameras and the booming popularity of video-sharing websites are the two leading contributors to the growth of computerized video analysis. A key challenge, however, is the sheer size of video data. To put this into perspective, one second of a high-definition video, in terms of size, is equivalent to over 2000 pages of text. Now consider that 100 hours of video are uploaded to YouTube every minute. Big data technologies turn this challenge into opportunity. Obviating the need for cost-intensive and risk-prone manual processing, big data technologies can be leveraged to automatically sift through hand draw intelligence from thousands of hours of video. As a result, the big data technology is the third factor that has contributed to the development of video analytics. The primary application of video analytics in recent years has been in automated security and surveillance systems. In addition to their high cost, labor-based surveillance systems tend to be less effective than automatic systems. Video analytics can efficiently and effectively perform surveillance functions such as detecting breaches of restricted zones, identifying objects removed or left unattended, detecting loitering in a specific area, recognizing suspicious activities, and detecting camera tampering, to name a few. Upon detection of a threat, the surveillance system may notify security personnel in real time or trigger an automatic action (e.g., sound alarm, lock doors, or turn on lights).