Region-Based Fully Convolutional Siamese Networks For Robust Real-Time Visual Tracking

Partial occlusions and deformations in visual object tracking are still very challenging. Existing Convolutional Neural Networks (CNNs) trackers either fail to handle these issues or can just run in low speed. In this paper, we present a real-time tracker which is robust to occlusions and deformations based on a Region-based, Fully Convolutional Siamese Network (R-FCSN). In the proposed R-FCSN, the information of regions is extracted separately by the proposition of position sensitive score maps. Combining these score maps via adaptive weights leads to accurate location of the target on a new frame. The experiments illustrate that our method outperforms state-of-the-art approaches, and can handle the cases of object deformation and occlusion at about 51 FPS.