Tracking Multiple High-Density Homogeneous Targets

  • Fabio Poiesi and Andrea Cavallaro. Tracking Multiple High-Density Homogeneous Targets. IEEE Transactions on Circuits and Systems for Video Technology, 25(4):623-637, 2014. doi:10.1109/TCSVT.2014.2344509
    [BibTeX] [Abstract]

    We present a framework for multi-target detection and tracking that infers candidate target locations in videos containing a high density of homogeneous targets. We propose a gradient-climbing technique and an isocontour slicing approach for intensity maps to localize targets. The former uses Markov Chain Monte Carlo to iteratively fit a shape model onto the target locations, whereas the latter uses the intensity values at different levels to find consistent object shapes. We generate trajectories by recursively associating detections with a hierarchical graphbased tracker on temporal windows. The solution to the graph is obtained with a greedy algorithm that accounts for false positive associations. The edges of the graph are weighted with a likelihood function based on location information. We evaluate the performance of the proposed framework on challenging datasets containing videos with high density of targets and compare it with six alternative trackers.

    @Article{2014-07-POIESI-2,
    author = {Fabio Poiesi and Andrea Cavallaro},
    title = {{Tracking Multiple High-Density Homogeneous Targets}},
    journal = {{IEEE Transactions on Circuits and Systems for Video Technology}},
    date = {2014-07},
    volume = {25},
    number = {4},
    pages = {623-637},
    doi = {10.1109/TCSVT.2014.2344509},
    abstract = {We present a framework for multi-target detection and tracking that infers candidate target locations in videos containing a high density of homogeneous targets. We propose a gradient-climbing technique and an isocontour slicing approach for intensity maps to localize targets. The former uses Markov Chain Monte Carlo to iteratively fit a shape model onto the target locations, whereas the latter uses the intensity values at different levels to find consistent object shapes. We generate trajectories by recursively associating detections with a hierarchical graphbased tracker on temporal windows. The solution to the graph is obtained with a greedy algorithm that accounts for false positive associations. The edges of the graph are weighted with a likelihood function based on location information. We evaluate the performance of the proposed framework on challenging datasets containing videos with high density of targets and compare it with six alternative trackers.},
    year = {2014}
    }

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