Curve and surface fitting with splines
Curve and surface fitting with splines
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi Feature Path Modeling for Video Surveillance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Detecting Irregularities in Images and in Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Independent increment processes for human motion recognition
Computer Vision and Image Understanding
Efficient projections onto the l1-ball for learning in high dimensions
Proceedings of the 25th international conference on Machine learning
Learning semantic scene models by trajectory analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Trajectory analysis plays a key role in human activity recognition and video surveillance. This paper proposes a new approach based on modeling trajectories using a bank of vector (velocity) fields. We assume that each trajectory is generated by one of a set of fields or by the concatenation of trajectories produced by different fields. The proposed approach constitutes a space-varying framework for trajectory modeling and is able to discriminate among different types of motion regimes. Furthermore, the vector fields can be efficiently learned from observed trajectories using an expectation-maximization algorithm. An experiment with real data illustrates the promising performance of the method.