Refiner: a general object position refinement algorithm for visual tracking

Object tracking is an important topic in computer vision. Most existing trackers require an accurate initial position of the target. However, in the real application, the initial location for tracking may not be accurate, which may lead to tracking drift. To solve this problem, we propose a simple deep learning-based method called Refiner that can produce the accurate position of an object given its rough location. Specifically, we propose an end-to-end position refinement network that consists of a backbone network, a feature enhancement module, a feature fusion module, and a shape predictor; the shape predictor includes two branches: a bounding box prediction branch and a mask prediction branch. We improve the spatial robustness of existing trackers by correcting the inaccurate initial position. In addition, the proposed method can also be used in the tracking process to improve the accuracy of the subsequent tracking results. Lots of experiments on the object tracking benchmarks verify its effectiveness and efficiency.