We present a practical framework for detecting and modeling 3D static occlusions for wide-baseline, multi-camera scenarios where the number of cameras is small. The framework consists of an iterative learning procedure where at each frame the occlusion model is used to solve the voxel occupancy problem, and this solution is then used to update the occlusion model. Along with this iterative procedure, there are two contributions of the proposed work: (1) a novel energy function (which can be minimized via graph cuts) specifically designed for use in this procedure, and (2) an application that incorporates our probabilistic occlusion model into a 3D tracking system. Both qualitative and quantitative results of the proposed algorithm and its incorporation with a 3D tracker are presented for support.

We present a computer vision system for robust object tracking in 3D by combining evidence from multiple calibrated cameras. This kernel-based 3D tracker is automatically bootstrapped by constructing 3D point clouds. These points clouds are then clustered and used to initialize the trackers and validate their performance. The framework describes a complete tracking system that fuses appearance features from all available camera sensors and is capable of automatic initialization and drift detection. Its elegance resides in its inherent ability to handle problems encountered by various 2D trackers, including scale selection, occlusion, view-dependence, and correspondence across views. Tracking results for an indoor smart room and a multi-camera outdoor surveillance scenario are presented. We demonstrate the effectiveness of this unified approach by comparing its performance to a baseline 3D tracker that fuses results of independent 2D trackers, as well as comparing the re-initialization results to known ground truth.