Partial tracking method based on siamese network

Robust object tracking is still a challenging task in the field of computer vision and has application value in many fields such as automatic driving, human–computer interaction and robot visual navigation. More and more researchers are devoted to researching more accurate object tracking methods. How to better deal with occlusion and deformation has always been the difficult challenges in the object tracking field, and the existing methods cannot solve these problems well. In this regard, we propose a novel, effective and portable module called part-based tracking and assembly (PTA), which is added to the fully convolutional siamese networks to divide the exemplar feature map into several parts. Each part is separately tracked, and then the tracking results of all parts are assembled to obtain the final tracking results. And the experiments on several popular tracking benchmarks show our variant trackers with the PTA module that operate at almost the same tracking speed with the original trackers and achieve superior tracking performance. Moreover, the tracking accuracy is significantly improved on the data with occlusion, deformation and background clutter. Compared with some real-time tracking methods, our variant trackers with the PTA module can achieve the state-of-the-art performance.