People detection in single 2D images has improved greatly in recent years. However, comparatively little of this progress has percolated into multi-camera multi-people tracking algorithms, whose performance still degrades severely when scenes become very crowded. In this work, we introduce a new architecture that combines Convolutional Neural Nets and Conditional Random Fields to explicitly model those ambiguities. One of its key ingredients are high-order CRF terms that model potential occlusions and give our approach its robustness even when many people are present. Our model is trained end-to-end and we show that it outperforms several state-of-art algorithms on challenging scenes.
@article{arxiv.1704.05775,
title = {Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection},
author = {Pierre Baqué and François Fleuret and Pascal Fua},
journal= {arXiv preprint arXiv:1704.05775},
year = {2017}
}