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

Lucas Beyer, Stefan Breuers, Vitaly Kurin, Bastian Leibe
arXiv:1705.04608

TL;DR: Explorative paper. Learn a Triplet-ReID net, embed the full image. Keep embeddings of known tracks, correlate them with image embeddings and use that as measurement model in a Bayesian filtering tracker. MOT score is mediocre, but framework is theoretically pleasing.

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.

» Show BibTeX
@article{BeyerBreuers2017Arxiv, author = {Lucas Beyer and Stefan Breuers and Vitaly Kurin and Bastian Leibe}, title = {Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters}, journal = {arXiv preprint arXiv:1705.04608}, year = {2017}, }



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