A New Algorithm for Labeling of Human Motion

In this paper, we present a novel approach for the labeling of human motion based on a probabilistic model of body features and Constraint-Based Genetic Algorithm (CBGA), which learns the set of conditional independence relations among the body features through a fitness function. The approach allows the user to add custom rules to produce valid candidate solutions to achieve more accurate results with constraint-based genetic operators. We also extend these results to learning the probabilistic structure of human body to improve the labeling results, the handling of missing body parts, and the integration of multi-frame information to improve the accuracy rates. Finally, we analyze the performance of our proposed approach and show that it outperforms most of the current state of the art methods on a set of motion captured walking, running and dancing sequences in terms of quality and robustness.