Optimal Algorithm for Shape from Shading and Path Planning
Journal of Mathematical Imaging and Vision
Real-Time Multiple Objects Tracking with Occlusion Handling in Dynamic Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Counting Crowded Moving Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
Modelling Crowd Scenes for Event Detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Animation and Virtual Worlds - CASA 2007
A Variational Technique for Time Consistent Tracking of Curves and Motion
Journal of Mathematical Imaging and Vision
Floor Fields for Tracking in High Density Crowd Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Tracking multiple humans in crowded environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Analyzing the crowd dynamics from video sequences is an open challenge in computer vision. Under a high crowd density assumption, we characterize the dynamics of the crowd flow by two related information: velocity and a disturbance potential which accounts for several elements likely to disturb the flow (the density of pedestrians, their interactions with the flow and the environment). The aim of this paper to simultaneously estimate from a sequence of crowded images those two quantities. While the velocity of the flow can be observed directly from the images with traditional techniques, this disturbance potential is far more trickier to estimate. We propose here to couple, through optimal control theory, a dynamical crowd evolution model with observations from the image sequence in order to estimate at the same time those two quantities from a video sequence. For this purpose, we derive a new and original continuum formulation of the crowd dynamics which appears to be well adapted to dense crowd video sequences. We demonstrate the efficiency of our approach on both synthetic and real crowd videos.