Abstract
A target tracking algorithm based on joint probabilistic data association encounters the problems of target loss and combinatorial explosion in cluttered and target-intensive tracking environments. In order to address these challenges, this paper proposes a video target tracking algorithm based on significance joint probabilistic data association. This algorithm detects the moving target in a video sequence through significance calculation. The detection results are classified, and the returned classes are then used as valid echoes for data association. Meanwhile, the validation matrix and joint probability in the joint probabilistic data association algorithm are redefined based on the significance information and a validation matrix and association probability based on significance is proposed. The association results are compensated and corrected using the colour association probability. Experiments in real scenes demonstrate that the proposed algorithm can suppress clutter in the background, simplify interconnected events, and solve the problem of computational combinatorial explosion.
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