Low Density Feature Point Matching for Articulated Pose Identification

We describe a general algorithm for identifying an arbitrary pose of an articulated subject with low density feature points. The algorithm aims to establish a one-to-one correspondence between two data point-sets, one representing the model of an observed subject and the other representing the pose taken from freeform motion of the subject. We avoid common assumptions such as pose similarity or small motion with respect to the model, and assume no prior knowledge from which to infer an initial or partial correspondence between the two point-sets. The algorithm integrates local segment-based correspondences under a set of affine transformations, and a global hierarchical search strategy. Experimental results, based on synthetic pose and real-world human motion capture data demonstrate the ability of the algorithm to perform the identification task. Reliability is compromised as noisy data and limited segmental distortion are increased, but the algorithm can tolerate moderate levels. Th...

• Type: Conference
• Year: 2002
• Where: BMVC
• Authors: Horst Holstein, Baihua Li
• Keywords: Algorithm, Arbitrary Pose, BMVC 2002, Computer Vision, General Algorithm