Moving object recognition in eigenspace representation: gait analysis and lip reading
Pattern Recognition Letters
Parameterized modeling and recognition of activities
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
Automatic Visual Recognition of Armed Robbery
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Recognition of Human Interaction Using Multiple Features in Grayscale Images
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Statistical Analysis of Dynamic Actions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analyzing Human Movements from Silhouettes Using Manifold Learning
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Journal of Cognitive Neuroscience
VSMM'07 Proceedings of the 13th international conference on Virtual systems and multimedia
Motion Flow-Based Video Retrieval
IEEE Transactions on Multimedia
Video-Based Human Movement Analysis and Its Application to Surveillance Systems
IEEE Transactions on Multimedia
IEEE Transactions on Circuits and Systems for Video Technology
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Recognizing human action is a critical step in many computer vision applications. In this paper, the problem of human behavior classification is addressed from a periodic motion analysis viewpoint. Our approach uses human silhouettes as motion features that can be obtained efficiently, and then projected it into a lower dimensional space where matching is performed. After a periodic analysis, each action unit is represented as a closed loop in this lower dimensional space, and matching is done by computing the distances among these loops. The main contributions are twofold: (1) an efficient periodic action feature constructing method is introduced; and (2) the difference between action units with different phase is computed adaptively with a novel distance proposed in this work. To demonstrate the effectiveness of this approach, human behavior classification experiments were performed on an open dataset. Classification results are highly accurate and show that this approach is promising and efficient.