Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Detection and Recognition of Periodic, Nonrigid Motion
International Journal of Computer Vision
View-Invariant Analysis of Cyclic Motion
International Journal of Computer Vision
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Tracking a group of highly correlated targets
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Multi-scale entropy analysis of dominance in social creative activities
Proceedings of the international conference on Multimedia
Human activity analysis: A review
ACM Computing Surveys (CSUR)
Stochastic Representation and Recognition of High-Level Group Activities
International Journal of Computer Vision
Group behavior recognition in context-aware systems
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Editors Choice Article: Tracking highly correlated targets through statistical multiplexing
Image and Vision Computing
International Journal of Computer Vision
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Most work in human activity recognition is limited to relatively simple behaviors like sitting down, standing up or other dramatic posture changes. Very little has been achieved in detecting more complicated behaviors especially those characterized by the collective participation of several individuals. In this work we present a novel approach to recognizing the class of activities characterized by their rigidity in formation for example people parades, airplane flight formations or herds of animals. The central idea is to model the entire group as a collective rather than focusing on each individual separately. We model the formation as a 3D polygon with each corner representing a participating entity. Tracks from the entities are treated as tracks of feature points on the 3D polygon. Based on the rank of the track matrix we can determine if the 3D polygon under consideration behaves rigidly or undergoes non-rigid deformation. Our method is invariant to camera motion and does not require an a priori model or a training phase.