Human Posture Recognition with Convex Programming

We present a novel human posture recognition method using convex programming based matching schemes. Instead of trying to segment the object from the background, we develop a novel multi-stage linear programming scheme to locate the target by searching for the best matching region based on an automatically acquired graph template. The linear programming based visual matching scheme generates relatively dense matching patterns and thus presents a key for robust object matching and human posture recognition. By matching distance transformations of edge maps, the proposed scheme is able to match figures with large appearance changes. We further present object recognition methods based on the similarity of the exemplar with the matching target. The proposed scheme can also be used for recognizing multiple targets in an image. Experiments show promising results for recognizing human postures in cluttered environments.

• Type: Conference
• Year: 2005
• Where: ICMCS
• Authors: Hao Jiang, Ze-Nian Li, Mark S. Drew
• Keywords: Human Posture Recognition, ICMCS 2005, Robust Object Matching, Visual Matching Scheme