Real-time recognition of activity using temporal templates
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Determining Optical Flow
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Detection of abnormal behaviors using a mixture of Von Mises distributions
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
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
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models
IEEE Transactions on Image Processing
Motion-based video retrieval by trajectory matching
IEEE Transactions on Circuits and Systems for Video Technology
Summarizing high-level scene behavior
Machine Vision and Applications
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This article presents a method to learn common motion patterns in video scenes, in order to detect abnormal behaviors or rare events based on global motion. The motion orientations are observed and learned, providing a common motion map. As in the background modeling technique using codebooks [1], we store motion information in a motion map. The motion map is then projected on various angles, allowing an easy visualization of common motion patterns. The motion map is also used to detect abnormal or rare events.