Proceedings of the 11th international conference on Intelligent user interfaces
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Weighted ensemble boosting for robust activity recognition in video
Machine Graphics & Vision International Journal
State-of-the-art on spatio-temporal information-based video retrieval
Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A survey on vision-based human action recognition
Image and Vision Computing
Boosting with temporal consistent learners: an application to human activity recognition
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Feature set search space for fuzzyboost learning
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
A comprehensive study of visual event computing
Multimedia Tools and Applications
Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models
International Journal of Computer Vision
Human action recognition using non-separable oriented 3D dual-tree complex wavelets
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Facial expression recognition using spatiotemporal boosted discriminatory classifiers
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Explaining Activities as Consistent Groups of Events
International Journal of Computer Vision
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
Max-Margin Early Event Detectors
International Journal of Computer Vision
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This paper contributes a new boosting paradigm to achieve detection of events in video. Previous boosting paradigms in vision focus on single frame detection and do not scale to video events. Thus new concepts need to be introduced to address questions such as determining if an event has occurred, localizing the event, handling same action performed at different speeds, incorporating previous classifier responses into current decision, using temporal consistency of data to aid detection and recognition. The proposed method has the capability to improve weak classifiers by allowing them to use previous history in evaluating the current frame. A learning mechanism built into the boosting paradigm is also given which allows event level decisions to be made. This is contrasted with previous work in boosting which uses limited higher level temporal reasoning and essentially makes object detection decisions at the frame level. Our approach makes extensive use of temporal continuity of video at the classifier and detector levels. We also introduce a relevant set of activity features. Features are evaluated at multiple zoom levels to improve detection. We show results for a system that is able to recognize 11 actions.