Robust visual tracking via context objects computing

Occlusions are challenging issue for robust visual tracking. In this paper, motivated by the fact that a tracked object is usual- ly embedded into context that provides useful information for estimating the target, we propose a novel tracking algorithm named Tracking with Context Prediction (TCP). The context here includes the neighboring objects and specific parts of tar- get. The proposed method simultaneously track the target and context objects using the existing tracking methods. The positions of the context objects are used to predict the position of the target. Thus, the target can be stably tracked even when it is partially or fully occluded. By computing the probability of each prediction being target, our algorithm allows the drifting of context objects during tracking and do not require predictions from all context objects are correct. Experiments on challenging sequences show significant improvements especially in the case of occlusions and appearance changes.