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arXiv · 1705.04608

Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters

Lucas Beyer·Stefan Breuers·Vitaly Kurin·Bastian Leibe
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Abstract

With the rise of end-to-end learning through deep learning, person detectors and re-identification (ReID) models have recently become very strong. Multi-camera multi-target (MCMT) tracking has not fully gone through this transformation yet. We intend to take another step in this direction by presenting a theoretically principled way of integrating ReID with tracking formulated as an optimal Bayes filter. This conveniently side-steps the need for data-association and opens up a direct path from full images to the core of the tracker. While the results are still sub-par, we believe that this new, tight integration opens many interesting research opportunities and leads the way towards full end-to-end tracking from raw pixels.

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Lucas Beyer, Stefan Breuers, Vitaly Kurin, Bastian Leibe. 2017-05-12. Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters. https://arxiv.org/abs/1705.04608

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