A Multi-Patch Network for Non-Rigid Object Tracking

Non-rigid object tracking is an important yet challenging task in computer vision. In this paper, a multi-patch neural network (MPNet) model is presented to address the problem of non-rigid object tracking. The model learns a multiple patch based framework, which mainly consists of two branches of neural networks. One branch is to track the global target and the other branch partitions the target into multiple patches which are tracked separately. The global tracking and the multiple patch tracking are combined to compute the final tracking results. Compared with the existing methods, our model exploits the trajectories of various parts of a non-rigid object and therefore can accurately track the non-rigid object. Experiments on visual tracking datasets prove the strength of the proposed method.