3D silhouette tracking with occlusion inference
2010
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 re-projected 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.
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