Unsupervised Bayesian Detection of Independent Motion in Crowds
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Machine Vision and Applications
Floor Fields for Tracking in High Density Crowd Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Journal of Systems and Software
Tracking multiple humans in crowded environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Action Recognition in Videos Using Nonnegative Tensor Factorization
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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On July 24, 2010, 21 people died and more than 500 were injured in a stampede at the Loveparade, a music festival, in Duisburg, Germany. Although this tragic incident is but one among many terrible crowd disasters that occur during pilgrimage, sports events, or other mass gatherings, it stands out for it has been well documented: there were a total of seven security cameras monitoring the Loveparade and the chain of events that led to disaster was meticulously reconstructed. In this paper, we present an automatic, video-based analysis of the events in Duisburg. While physical models and simulations of human crowd behavior have been reported before, to the best of our knowledge, automatic vision systems that detect congestions and dangerous crowd turbulences in real world settings were not reported yet. Derived from lessons learned from the video footage of the Loveparade, our system is able to detect motion patterns that characterize crowd behavior in stampedes. Based on our analysis, we propose methods for the detection and early warning of dangerous situations during mass events. Since our approach mainly relies on optical flow computations, it runs in real-time and preserves privacy of the people being monitored.