A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recursive implementation of the Gaussian filter
Signal Processing
Tracking Points on Deformable Objects Using Curvature Information
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Using Particles to Track Varying Numbers of Interacting People
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Hidden Markov Models for Optical Flow Analysis in Crowds
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Mining paths of complex crowd scenes
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Self-Organizing Maps for the Automatic Interpretation of Crowd Dynamics
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
A quantitative comparison of two new motion estimation algorithms
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
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This paper presents a new motion estimation method aimed at crowd scene analysis in complex video sequences. The proposed technique makes use of image descriptors extracted from points lying at the maximum curvature on the Canny edge map of an analyzed image. Matches between two consecutive frames are then carried out by searching for descriptors that satisfy both a well-defined similarity metric and a structural constraint imposed by the edge map. A preliminary assessment using real-life video sequences gives both qualitative and quantitative results.