CAT: Corner Aided Tracking With Deep Regression Network

Abstract

Single object tracking in visual media is an important yet challenging task. Various challenges, especially target scale variation, shape deformation and occlusion, can have large effects on the performances of trackers. Current deep regression based trackers only pay close attention to regression on the center key point of the tracking target, meanwhile employ the image pyramid based multi-scale testing method to deal with scale estimation. Such procedure can not properly handle the three challenges. We address these challenges in a principled way by the aid of auxiliary regressions on the four bounding box corners of the tracking target. In this work, we propose the novel Corner Aided Tracker with deep regression network, abbreviated as CAT. Different from RPN-based trackers, in CAT, four corners along with the center key point of the bounding box for tracking target are simultaneously obtained by five corresponding response maps. Furthermore, to robustly and accurately generate tight bounding boxes for the tracking target and collect reliable samples for online training of the network, we propose an adaptive key point selection method to select the subset of reliable key points and drop the unreliable ones, based on the qualities of their corresponding response maps as well as the constraints from shape, scale and location. We demonstrate that the regressed corners can help naturally locate the tracking target with tight bounding boxes. The challenges of scale variation, shape deformation and occlusion can be handled explicitly. The commonly used time-consuming image pyramid based multi-scale testing method can also be discarded. Extensive experiments on OTB2013, OTB2015, UAV123, LaSOT, VOT2016 and VOT2018 datasets are conducted to report new state-of-the-art performances and demonstrate the effectiveness of CAT.