Online 3D tracking of human arms with a single camera

This research presents a 3-D human arms tracking method with a monocular camera. In our previous work, multiple clues have been integrated by the multiple importance sampling particle filter to track the arms with arbitrary motion on the images. Due to the lack of depth information when using a monocular camera, an online sequential pose estimation based on the structure-from-motion is proposed here to provide the 3D arm posture hypotheses of multiple importance sampling particle filter. In our 3D sequential pose estimating algorithm, the structure of each arm parts is priory assumed to be a volumetric model with the points uniformly distributed on its surface. The 3D motion of arms can be recovered more reliably by visual tracking of the points on the known structure of each arm. In addition, while the objects in view appear larger when they are closer to the camera, the size effect from perspective projection is considered to disambiguate the arm posture. The posture hypotheses from the multiple importance sampling are finally verified by 2D visual features and the 3D posture information augmented with the size effect. The robustness and efficiency of the proposed algorithm have been validated in the experiments.