Tracking a large number of objects from multiple views

We propose a multi-object multi-camera framework for tracking large numbers of tightly-spaced objects that rapidly move in three dimensions. We formulate the problem of finding correspondences across multiple views as a multidimensional assignment problem and use a greedy randomized adaptive search procedure to solve this NPhard problem efficiently. To account for occlusions, we relax the one-to-one constraint that one measurement corresponds to one object and iteratively solve the relaxed assignment problem. After correspondences are established, object trajectories are estimated by stereoscopic reconstruction using an epipolar-neighborhood search. We embedded our method into a tracker-to-tracker multi-view fusion system that not only obtains the three-dimensional trajectories of closely-moving objects but also accurately settles track uncertainties that could not be resolved from single views due to occlusion. We conducted experiments to validate our greedy assignment procedure and ...

• Type: Journal
• Year: 2009
• Where: ICCV
• Authors: Zheng Wu, Nickolay I. Hristov, Tyson L. Hedrick, Thomas H. Kunz, Margrit Betke
• Keywords: Assignment Problem, Computer Vision, Greedy Randomized Adaptive Search Procedure, ICCV 2009, Multidimensional Assignment Problem