Efficient content analysis engine for visual surveillance network
IEEE Transactions on Circuits and Systems for Video Technology
Adaptive multiple object tracking using colour and segmentation cues
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
An adaptive Bayesian technique for tracking multiple objects
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Ant clustering PHD filter for multiple-target tracking
Applied Soft Computing
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For applications such as behavior recognition it is important to maintain the identity of multiple targets, while tracking them in the presence of splits and merges, or occlusion of the targets by background obstacles. Here we propose an algorithm to handle multiple splits and merges of objects based on dynamic programming and a new geometric shape matching measure. We then cooperatively combine Kalman filter-based motion and shape tracking with the efficient and novel geometric shape matching algorithm. The system is fully automatic and requires no manual input of any kind for initialization of tracking. The target track initialization problem is formulated as computation of shortest paths in a directed and attributed graph using Dijkstra's shortest path algorithm. This scheme correctly initializes multiple target tracks for tracking even in the presence of clutter and segmentation errors which may occur in detecting a target. We present results on a large number of real world image sequences, where upto 17 objects have been tracked simultaneously in real-time, despite clutter, splits, and merges in measurements of objects. The complete tracking system including segmentation of moving objects works at 25 Hz on 352times288 pixel color image sequences on a 2.8-GHz Pentium-4 workstation