The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of the joint probability of multisensory signals
Pattern Recognition Letters
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Inference of Human Postures by Classification of 3D Human Body Shape
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Human action-recognition using mutual invariants
Computer Vision and Image Understanding
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
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
A new shape descriptor defined on the Radon transform
Computer Vision and Image Understanding
A fused hidden Markov model with application to bimodal speech processing
IEEE Transactions on Signal Processing
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Tracking and activity recognition through consensus in distributed camera networks
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Recognizing activities in multiple views with fusion of frame judgments
Image and Vision Computing
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More and more researchers focus their studies on multiview activity recognition, because a fixed view could not provide enough information for recognition. In this paper, we use multi-view features to recognize six kinds of gymnastic activities. Firstly, shape-based features are extracted from two orthogonal cameras in the form of R transform. Then a multi-view approach based on Fused HMM is proposed to combine different features for similar gymnastic activity recognition. Compared with other activity models, our method achieves better performance even in the case of frame loss.