Density-aware person detection and tracking in crowds
- 1 November 2011
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 2423-2430
- https://doi.org/10.1109/iccv.2011.6126526
Abstract
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.Keywords
This publication has 20 references indexed in Scilit:
- Data-driven crowd analysis in videosPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Object Detection with Discriminatively Trained Part-Based ModelsIeee Transactions On Pattern Analysis and Machine Intelligence, 2009
- Tracking in unstructured crowded scenesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Putting Objects in PerspectiveInternational Journal of Computer Vision, 2008
- SPECIFICATION OF THE SOCIAL FORCE PEDESTRIAN MODEL BY EVOLUTIONARY ADJUSTMENT TO VIDEO TRACKING DATAAdvances in Complex Systems, 2007
- Objects in ContextPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Multi-Target Tracking - Linking Identities using Bayesian Network InferencePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Counting Crowded Moving ObjectsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- A Viewpoint Invariant Approach for Crowd CountingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- MCMC-based particle filtering for tracking a variable number of interacting targetsIeee Transactions On Pattern Analysis and Machine Intelligence, 2005