Data-driven Crowd Analysis

Mikel Rodriguez, Josef Sivic, Ivan Laptev, Jean-Yves Audibert

To appear in ICCV 2011:

We address the problem of person detection and tracking in crowded video scenes. While the detection of individual objects has been improved significantly over the recent years, crowd scenes remain particularly challenging for the detection and tracking tasks due to heavy occlusions, high person densities and significant variation in people's appearance. To address these challenges, we propose to leverage information on the global structure of the scene and to resolve all detections simultaneously. In particular, we explore constraints imposed by the crowd density and formulate person detection as the optimization of a joint energy function combining crowd density estimation and the localization of individual people. We demonstrate how the optimization of such an energy function significantly improves person detection and tracking in crowds.
We validate our approach on a challenging video dataset of crowded scenes.