Selecting best viewpoint for human-pose estimation

Estimating human poses is an important step towards developing robots that can understand human motion. Since a human is highly articulated, changing viewpoints of sensors on robots can improve the accuracy of human-pose estimation. We propose a two-phase approach that determines the best viewpoint of a depth sensor for human-pose estimation. The proposed approach measures the quality of potential viewpoints and selects one of them as the best viewpoint for each human pose. Based on the quality of viewpoints, human poses can be directly mapped to the best viewpoint without reconstructing the human body. Thus, the proposed approach provides a discriminative mapping to determine the best viewpoint for estimating different human poses. To measure the quality of a potential viewpoint, the viewpoint is first instantiated by representing the depth sensor of the viewpoint using the finite projective camera model. The quality of the viewpoint is expressed in terms of the error of humanpose estimates. A mapping is derived by minimizing the error in a human-pose estimate among different viewpoints. The proposed two-phase approach has been evaluated on a benchmark database. Experimental results showed that the best viewpoint for a human pose could be determined by evaluating the quality of potential viewpoints. The mean error and standard deviation of human-pose estimates were reduced by using the best viewpoint determined by the proposed two-phase approach.