Abstract
We present a new global optimization approach for multiple people tracking based on a hierarchical tracklet framework. A new type of tracklets is introduced, which we call tree tracklets. They contain bifurcations to naturally deal with ambiguous tracking situations. Difficult decisions are postponed to a later iteration of the hierarchical framework, when more information is available. We cast the optimization problem as a minimum cost arborescence problem in an acyclic directed graph, where a tracking solution can be obtained in linear time. Experiments on six publicly available datasets show that the method performs well when compared to state-of-the art tracking algorithms.
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Notes
- 1.
The code is publicly available: http://www.tnt.uni-hannover.de/project/MPT/.
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Henschel, R., Leal-Taixé, L., Rosenhahn, B. (2014). Efficient Multiple People Tracking Using Minimum Cost Arborescences. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_21
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DOI: https://doi.org/10.1007/978-3-319-11752-2_21
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