LAUSR

Object tracking has been a hot computer vision topic for many years. Although great process has been made, it still has large room to improve because of the complexity of the natural scene and the multiple interference. In this work, we improve the object tracking performance in two ways. First, a sequential scoring model is proposed to integrate the optical flow information of history video frames into the feature map of current frame. Second, an attention model with optical flow information is used for further improvement by differentiating the contribution of different positions in the template to the final response map. On the other hand, the entire model are end-to-end trainable. We test the methods on OTB (Object Tracking Benchmark) and VOT (Visual Object Tracking) tracking datasets. The experimental results demonstrate that the improved tracking accuracy and robustness to occlusion, strenuous motion and vanishing objects.