Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
Simultaneous Estimation of Segmentation and Shape
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Segmentation of a Vector Field: Dominant Parameter and Shape Optimization
Journal of Mathematical Imaging and Vision
Segmentation of Discrete Vector Fields
IEEE Transactions on Visualization and Computer Graphics
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
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Crowd movement analysis has many practical applications, especially for video surveillance. The common methods are based on pedestrian detection and tracking. With an increase of crowd density, however, it is difficult for these methods to analyze crowd movement because of the computation and complexity. In this paper, a novel approach for crowd flow segmentation is proposed. We employ a Weighting Fuzzy C-Means clustering algorithm (WFCM) to extract the motion region in optical flow field. In order to further analyze crowd movement, we make use of translation flow to approximate local crowd movement, and design a shape derivative based region growing scheme to segment the crowd flows. In the experiments, the proposed method is tested on a set of crowd video sequences from low density to high density.