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Multi-Commodity Network Flow for Tracking Multiple People
Aug. 2014 (vol. 36 no. 8)
pp. 1-1
Horesh Ben Shitrit, , École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Jerome Berclaz, , Microsoft, Sunnyvale, CA, USA
Francois Fleuret, , École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Pascal Fua, , École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
In this paper, we show that tracking multiple people whose paths may intersect can be formulated as a multi-commodity network flow problem. Our proposed framework is designed to exploit image appearance cues to prevent identity switches. Our method is effective even when such cues are only available at distant time intervals. This is unlike many current approaches that depend on appearance being exploitable from frame-to-frame. Furthermore, our algorithm lends itself to a real-time implementation. We validate our approach on three publicly available datasets that contain long and complex sequences, the APIDIS basketball dataset, the ISSIA soccer dataset, and the PETS'09 pedestrian dataset. We also demonstrate its performance on a newer basketball dataset that features complete world championship basketball matches. In all cases, our approach preserves identity better than state-of-the-art tracking algorithms.
Index Terms:
Trajectory,Radar tracking,Linear programming,Optimization,Target tracking,Real-time systems,Linear Programming,Multi-object tracking,Multi-Commodity Network Flow,MCNF,Tracklet association
Citation:
Horesh Ben Shitrit, Jerome Berclaz, Francois Fleuret, Pascal Fua, "Multi-Commodity Network Flow for Tracking Multiple People," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 8, pp. 1-1, Aug. 2014, doi:10.1109/TPAMI.2013.210
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