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.

Occlusionrobust Deformable Object Tracking without Physics Simulation Estimating the state of a deformable object is crucial for robotic manipulation, yet accurate tracking is challenging when the object is partiallyoccluded. To address this problem, we propose an occlusionrobust RGBD sequence tracking framework based on Coherent Point Drift (CPD). To mitigate the effects of occlusion, our method 1) Uses a combination of locally linear embedding and constrained optimization to regularize the output of CPD, thus enforcing topological consistency when occlusions create disconnected pieces of the object; 2) Reasons about the freespace visible by an RGBD sensor to better estimate the prior on point location and to detect tracking failures during occlusion; and 3) Uses shape descriptors to find the most relevant previous state of the object to use for tracking after a severe occlusion. Our method does not rely on physics simulation or a physical model of the object, which can be difficult to obtain in unstructured environments. Despite having no physical model, our experiments demonstrate that our method achieves improved accuracy in the presence of occlusion as compared to a physicsbased CPD method while maintaining adequate runtime.