Employing Shadows for MultiPerson Tracking Based on a Single RGBD Camera Although there are many algorithms to track people that are walking, existing methods mostly fail to cope with occluded bodies in the setting of multiperson tracking with one camera. In this paper, we propose a method to use peoplexe2x80x99s shadows as a clue to track them instead of treating shadows as mere noise. We introduce a novel method to track multiple people by fusing shadow data from the RGB image with skeleton data, both of which are captured by a single RGB Depth (RGBD) camera. Skeletal tracking provides the positions of people that can be captured directly, while their shadows are used to track them when they are no longer visible. Our experiments confirm that this method can efficiently handle full occlusions. It thus has substantial value in resolving the occlusion problem in multiperson tracking, even with other kinds of cameras.

3D Silhouette Tracking with Occlusion Inference It is a challenging problem to robustly track moving objects from image sequences because of occlusions. Previous methods did not exploit depth information sufficiently. Based on multiple camera scenes, we propose a 3D silhouette tracking framework to resolve occlusions and recover the appearances in 3D space, which enhances tracking effectiveness. In the framework, 2D object silhouettes are initially gained by Snake. Then a Voxel Space Carving procedure is introduced to simultaneously generate the occlusion model and visual hull of objects. Next, we adopt Particle Filter to select the valuable parts of occlusion model and combine them with the initial object silhouettes to generate the updated visual hull. Finally, updated visual hull of the objects are reprojected to each view to obtain their final contours. The experiments under the public LAB and SCULPTURE datasets validate the feasibility and effectiveness of our framework.