Multiple human tracking in high-density crowds
Image and Vision Computing
Tracking using motion patterns for very crowded scenes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Scene semantics from long-term observation of people
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Multi-target tracking on confidence maps: An application to people tracking
Computer Vision and Image Understanding
Multiple human tracking system for unpredictable trajectories
Machine Vision and Applications
Multimedia Tools and Applications
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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 jointly. 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.