Branch-and-price global optimization for multi-view multi-object tracking

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2012.

Laura Leal-Taixé, Gerard Pons-Moll and Bodo Rosenhahn


We present a new algorithm to jointly track multiple objects in multi-view images. While this has been typically addressed separately in the past, we tackle the problem as a single global optimization.
We formulate this assignment problem as a min-cost problem by defining a graph structure that captures both temporal correlations between objects as well as spatial correlations enforced by the configuration of the cameras. This leads to a complex combinatorial optimization problem that we solve using Dantzig-Wolfe decomposition and branching. Our formulation allows us to solve the problem of reconstruction and tracking in a single step by taking all available evidence into account.
In several experiments on multiple people tracking and 3D human pose tracking, we show our method outperforms state-of-the-art approaches.

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