Tracking multiple objects through occlusion with online sampling and position estimation

To track multiple objects through occlusion, either depth information of the scene or prior models of the objects such as spatial models and smooth/predictable motion models are usually assumed before tracking. When these assumptions are unreasonable, the tracker may fail. To overcome this limitation, we propose a novel online sample based framework, inspired by the fact that the corresponding local parts of objects in sequential frames are always similar in the local color and texture features and spatial features relative to the centers of objects. Experimental results illustrate that the proposed approach works robustly under difficult and complex conditions.