Multiple-Target Tracking by Spatiotemporal Monte Carlo Markov Chain Data Association

  title={Multiple-Target Tracking by Spatiotemporal Monte Carlo Markov Chain Data Association},
  author={Qian Yu and G{\'e}rard G. Medioni},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
We propose a framework for tracking multiple targets, where the input is a set of candidate regions in each frame, as obtained from a state-of-the-art background segmentation module, and the goal is to recover trajectories of targets over time. Due to occlusions by targets and static objects, as also by noisy segmentation and false alarms, one foreground region may not correspond to one target faithfully. Therefore, the one-to-one assumption used in most data association algorithms is not… CONTINUE READING
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