Human Body Parts Tracking
We propose a 2D model-based approach for tracking human body parts during articulated motion. A human is modeled as a stick figure with thirteen landmarks, and an action is a sequence of these stick figures. Given the locations of these joints in a model video and only the first frame of a test video, the joint locations are automatically estimated throughout the test video using two geometric constraints. The first constraint is based on the invariance of the ratio of areas under an affine transformation, and provides initial estimates. The second one is based on the fundamental matrix, defined by the corresponding landmarks of the two actors, and refines the initial estimates. Using these estimated locations, the tracking algorithm determines the exact location of each joint in the test video. The novelty of our approach lies in the geometric formulation of human actions and the use of geometric constraints for body joints estimation. The approach is able to handle variations in anthropometry of individuals, viewpoints, execution rate, and style of action execution. Experimental results provide encouraging quantitative and qualitative performance analysis.
Alexei Gritai, Arslan Basharat, and Mubarak Shah, Geometric Constraints on 2D Action Models for Tracking Human Body, International Conference on Pattern Recognition (ICPR), Tampa, FL, December 2008. (Oral presentation with ~18% acceptance rate)