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

Lucas Beyer, Stefan Breuers, Vitaly Kurin, Bastian Leibe
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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 = {{2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}},
year = {2017},
pages ={1444-1453},
}




Disclaimer Home Visual Computing institute RWTH Aachen University