Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
View-Invariant Analysis of Cyclic Motion
International Journal of Computer Vision
Human motion analysis: a review
Computer Vision and Image Understanding
Recognition of Visual Activities and Interactions by Stochastic Parsing
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
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
Free viewpoint action recognition using motion history volumes
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Human action-recognition using mutual invariants
Computer Vision and Image Understanding
A survey on vision-based human action recognition
Image and Vision Computing
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
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Action recognition is a popular and important research topic in computer vision. However, it is challenging when facing viewpoint variance. So far, most researches in action recognition remain rooted in view-dependent representations. Some view invariance approaches have been proposed, but most of them suffer from some weaknesses, such as lack of abundant information for recognition, dependency on robust meaningful feature detection or point correspondence. To perform viewpoint and subject independent action recognition, we propose a representation named "Envelop Shape" which is viewpoint insensitive. "Envelop Shape" is easy to acquire from silhouettes using two orthogonal cameras. It makes full use of two cameras' silhouettes to dispel influence caused by human body's vertical rotation, which is often the primary viewpoint variance. With the help of "Envelop Shape", we obtained inspiring results on action recognition independent of subject and viewpoint. Results indicate that "Envelop Shape" representation contains enough discriminating features for action recognition.