Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection

Pierre Baque, Francois Fleuret, Pascal Fua; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 271-279

People detection in 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 resolve 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-the-art algorithms on challenging scenes.