View-Invariant Modeling and Recognition of Human Actions Using Grammars

In this paper, we represent human actions as short sequences of atomic body poses. The knowledge of body pose is stored only implicitly as a set of silhouettes seen from multiple viewpoints; no explicit 3D poses or body models are used, and individual body parts are not identified. Actions and their constituent atomic poses are extracted from a set of multiview multiperson video sequences by an automatic keyframe selection process, and are used to automatically construct a probabilistic context-free grammar (PCFG). Given a new single viewpoint video, we can parse it to recognize actions and changes in viewpoint simultaneously. Experimental results are provided.

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Abhijit S. Ogale, Alap Karapurkar, Yiannis Aloimonos
• Keywords: Body Pose, Computer Vision, Constituent Atomic Poses, ECCV 2006, Individual Body Parts