Dynamic Self-Occlusion Avoidance Approach Based on the Depth Image Sequence of Moving Visual Object

Abstract

How to avoid the self-occlusion of a moving object is a challenging problem. An approach for dynamically avoiding self-occlusion is proposed based on the depth image sequence of moving visual object. Firstly, two adjacent depth images of a moving object are acquired and each pixel’s 3D coordinates in two adjacent depth images are calculated by utilizing antiprojection transformation. On this basis, the best view model is constructed according to the self-occlusion information in the second depth image. Secondly, the Gaussian curvature feature matrix corresponding to each depth image is calculated by using the pixels’ 3D coordinates. Thirdly, based on the characteristic that the Gaussian curvature is the intrinsic invariant of a surface, the object motion estimation is implemented by matching two Gaussian curvature feature matrices and using the coordinates’ changes of the matched 3D points. Finally, combining the best view model and the motion estimation result, the optimization theory is adopted for planning the camera behavior to accomplish dynamic self-occlusion avoidance process. Experimental results demonstrate the proposed approach is feasible and effective.