Project page for the Deep-Occlusion Multi-Camera Multi-People tracker.
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.
We provide a two minutes long people detection video illustrating the behaviour output of our method on the challenging and crowded new ETHZ dataset.
Deep-Occlusion reasoning + K-Shortest Path Tracking. Also available with top-view .
Technical details on learning gaussian mixture networks used in our discriminative model.
Visualization of the output of the discriminative model and the learned Gaussian classes.
We provide a short document which reviews the keys elements towards efficient impementation of Mean-Fields inference with our High-Order potentials.