Computer Vision and Image Understanding
Backtracking: Retrospective multi-target tracking
Computer Vision and Image Understanding
Robust hierarchical multiple hypothesis tracker for multiple-object tracking
Expert Systems with Applications: An International Journal
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In this paper, we present a new multiple hypothesestracking (MHT) approach. Our tracking method is suitablefor online applications, because it labels objects at everyframe and estimates the best computed trajectories up tothe current frame. In this work we address the problems ofobject merging and splitting (occlusions) and object fragmentations.Object fragmentation resulting from imperfectbackground subtraction can easily be confused with splittingobjects in a scene, especially in close range surveillanceapplications. This subject is not addressed in mostMHT methods. In this work, we propose a framework forMHT which distinguishes fragmentation and splitting usingtheir spatial and temporal characteristics and by generatinghypotheses only for splitting cases using observation inlater frames. This approach results in a more accurate dataassociation and a reduced size of the hypothesis graph. Ourtracking method is evaluated with various indoor videos.