View-invariant full-body gesture recognition from video

In this paper, we propose a video-based full-body gesture recognition system independent of the view angle of the cameras. We performed multilinear analysis on the silhouette images of the static poses making up the gestures by tensor decomposition and projection. Each pair of silhouette images is projected to a viewinvariant low dimensional pose coefficient vector space. These pose vectors are then used as input vectors in hidden Markov model (HMM) for gesture recognition. This system worked effectively in our experiments using real videos.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Bo Peng, Gang Qian, Stjepan Rajko
• Keywords: Computer Vision, Full-body Gesture Recognition, ICPR 2008, Pose Coefficient Vector, Pose Vectors