Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/84207
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Type: Conference paper
Title: Multi-target tracking by continuous energy minimization
Author: Milan, A.
Schindler, K.
Citation: Proceedings of the 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 11) / pp.1265-1272
Publisher: IEEE
Publisher Place: USA
Issue Date: 2011
ISBN: 9781457703942
Conference Name: IEEE Conference on Computer Vision and Pattern Recognition (24th : 2011 : Colorado Springs, CO, U.S.A.)
Statement of
Responsibility: 
Anton Andriyenko, Konrad Schindler
Abstract: We propose to formulate multi-target tracking as minimization of a continuous energy function. Other than a number of recent approaches we focus on designing an energy function that represents the problem as faithfully as possible, rather than one that is amenable to elegant optimization. We then go on to construct a suitable optimization scheme to find strong local minima of the proposed energy. The scheme extends the conjugate gradient method with periodic trans-dimensional jumps. These moves allow the search to escape weak minima and explore a much larger portion of the variable-dimensional search space, while still always reducing the energy. To demonstrate the validity of this approach we present an extensive quantitative evaluation both on synthetic data and on six different real video sequences. In both cases we achieve a significant performance improvement over an extended Kalman filter baseline as well as an ILP-based state-of-the-art tracker.
Rights: Copyright status unknown
RMID: 0020137020
DOI: 10.1109/CVPR.2011.5995311
Appears in Collections:Computer Science publications

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