Hierarchical abnormal event detection by real time and semi-real time multi-tasking video surveillance system

Authors:
Sung Chun Lee;Ram Nevatia
Affiliations:
Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, USA;Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, USA
Venue:
Machine Vision and Applications
Year:
2014

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Abstract

In this paper, we describe how to detect abnormal human activities taking place in an outdoor surveillance environment. Human tracks are provided in real time by the baseline video surveillance system. Given trajectory information, the event analysis module will attempt to determine whether or not a suspicious activity is currently being observed. However, due to real-time processing constrains, there might be false alarms generated by video image noise or non-human objects. It requires further intensive examination to filter out false event detections which can be processed in an off-line fashion. We propose a hierarchical abnormal event detection system that takes care of real time and semi-real time as multi-tasking. In low level task, a trajectory-based method processes trajectory data and detects abnormal events in real time. In high level task, an intensive video analysis algorithm checks whether the detected abnormal event is triggered by actual humans or not.