Kinematics-based tracking of human walking in monocular video sequences

摘要

Human tracking is currently one of the most active research topics in computer vision. This paper proposed a kinematics-based approach to recovering motion parameters of people walking from monocular video sequences using robust image matching and hierarchical search. Tracking a human with unconstrained movements in monocular image sequences is extremely challenging. To reduce the search space, we design a hierarchical search strategy in a divide-and-conquer fashion according to the tree-like structure of the human body model. Then a kinematics-based algorithm is proposed to recursively refine the joint angles. To measure the matching error, we present a pose evaluation function combining both boundary and region information. We also address the issue of initialization by matching the first frame to six key poses acquired by clustering and the pose having minimal matching error is chosen as the initial pose. Experimental results in both indoor and outdoor scenes demonstrate that our approach performs well.