Multi-view Gymnastic Activity Recognition with Fused HMM

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 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.

• Type: Conference
• Year: 2007
• Where: ACCV
• Authors: Ying Wang, Kaiqi Huang, Tieniu Tan
• Keywords: ACCV 2007, Computer Vision, Gymnastic Activity Recognition, Multi-view Activity Recognition, Multi-view Features