TRACKING HUNDREDS OF PEOPLE IN DENSELY CROWDED SCENES WITH PARTICLE FILTERING SUPERVISING DEEP CONVOLUTIONAL NEURAL NETWORKS

Tracking an entire high-density crowd composed of more than five hundred individuals is a difficult task that has not yet been accomplished. In this article, we propose to track pedestrians using a model composed of a Particle Filter {(PF)} and three Deep Convolutional Neural Networks (DCNN). The first network is a detector that learns to localize the persons. The second one is a pretrained network that estimates the optical flow, and the last one corrects the flow. Our contribution resides in the way we train this last network by {PF} supervision, and on Markov Random Field linking the different tracks.