Rapid and robust human detection and tracking based on omega-shape features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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This paper presents a robust human tracking system which incorporates automatic detection of head shape objects with decentralized tracking approach. A fast and robust probabilistic shape contour matching algorithm is applied to the input image frame to detect and locate head shape objects. The detected objects are then tracked by decentralized trackers. Here, a decentralized tracker refers to the tracker that tracks exactly one object. Essentially, each newly detected object will instantiate an individual tracker, which tracks the object and destroys itself when the object disappears. Two trackers communicate with each other only when they are getting close enough. This approach simplifies the competition of targets between trackers, and is more efficient than the centralized approach whose time complexity is greatly depends on the number of tracked objects. The system has been tested with several challenging digital surveillance video sequences, and the results show the robustness and the efficiency of the system under crowded and clutter environment.