Abstract More and more researchers focus their studies on multi-view 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 $\Re$ 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.
Multi-view Gymnastic Activity Recognition with Fused HMM
Computer Vision – ACCV 2007 ; 17 ; 667-677
Lecture Notes in Computer Science ; 4843 , 17
2007-01-01
11 pages
Article/Chapter (Book)
Electronic Resource
English
Recognition Rate , Activity Recognition , Frontal View , Hide State , Ground Truth Data Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence (incl. Robotics) , Biometrics , Algorithm Analysis and Problem Complexity
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