Video analytics technology has matured and found application in a variety of fields over the past decade. This chapter discusses the current state-ofthe-art, and describes challenges for future video analytics implementations. Current applications and markets for video analytics are described in the context of a processing pipeline. Application-specific challenges are described with potential solutions to those challenges. This chapter also lists some implementation considerations for embedded video analytics and concludes with future and emerging applications of video analytics.
IntroductionVideo analytics is an industry term for the automated extraction of information from video for a variety of purposes. It is a combination of imaging, computer vision, pattern analysis, and machine intelligence applied to real-world problems. Its utility spans several industry segments including video surveillance, retail, and transportation. Video analytics is distinct from machine vision or machine inspection and is similar to automotive vision. Some applications of analytics include the detection of suspicious objects and activities for offering better security, in license plate recognition and traffic analysis for intelligent transportation systems, and in customer counting and queue management for retail applications.The past decade has seen the maturation of algorithms and the adoption of analytics solutions in these markets. Analytics has progressed from research labs, with algorithms running on powerful workstations and PCs to current real-time embedded implementations on consumer-grade embedded processors. At the same time, the range of applications for analytics has also grown, with current trends indicating