Multi-Camera People Identification and Tracking

Horesh Ben Shitrit (Ecole Polytechnique Fédérale de Lausanne)
11.09.2013 - 14:00
Valid

Abstract:
In this talk, I will present the tracking system we developed at the CVLAB, EPFL. Our system is able to reliably track multiple people in a multi-camera setting. The obtained trajectories can be used for understanding individuals and group behavior. There are numerous applications for such a system; we are currently involved in a project whose goal is to understand the behavior of basketball teams and players from video cameras.

Our system represents the ground floor of the scene, as a grid of cells. The goal of the system is to estimate, at each time step, which grid cells are occupied and by whom.  The system is composed of three core components: detection, identification and tracking. Detection is based on a generative model which can effectively handle occlusions in each time frame independently. This produces what we call a Probability Occupancy Map (POM) which is then used by the next components. The identification component recognizes the identity of the person according to his color histogram, facial descriptor and, in the case of sport matches, his jersey number. In the final tracking component, the multi-people tracking problem is formulated as a multi-commodity network flow problem. The tracker links the detections of people in individual frames across time, while taking into account the appearance and identity constraints.

The full system demonstrates excellent results on long and challenging video
sequences, including a pedestrian benchmark dataset and several sports
datasets. We show that our system works reliably in spite of significant occlusions and delivers metrically accurate trajectories for each tracked individual.