View-Invariant Analysis of Cyclic Motion
International Journal of Computer Vision
Human motion analysis: a review
Computer Vision and Image Understanding
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
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
Invariant features for 3-D gesture recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human activity recognition for automatic visual surveillance of wide areas
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Human action-recognition using mutual invariants
Computer Vision and Image Understanding
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
View-Invariant Pose Recognition Using Multilinear Analysis and the Universum
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Recognizing body poses using multilinear analysis and semi-supervised learning
Pattern Recognition Letters
Recognizing activities in multiple views with fusion of frame judgments
Image and Vision Computing
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Human action recognition is a popular research area while it is changeling when facing various conditions related to viewpoint, subject, background, illumination and so on. Among all the variances, viewpoint variant is one of the most urgent problems to deal with. To this end, some view invariance approaches have been proposed, but they suffered from some weaknesses, such as lack of abundant information for recognition, dependency on robust meaningful feature detection or point correspondence. We propose a novel representation named “Envelop Shape”. We prove it from both theory and experiments that such representation is viewpoint insensitive. “Envelop Shape” is easy to acquire. It conveys abundant information enough for supporting action recognition directly. It also gets ride of the burdens such as feature detection and point correspondence, which are often difficult and error prone. In order to validate our proposed approach, we also present some experiments. With the help of “Envelop Shape”, our system achieves an impressive distinguishable result under different viewpoints