Bounding Box Distribution Learning and Center Point Calibration for Robust Visual Tracking

Abstract

Visual tracking aims at both robust target classification and accurate localization. However, the reliability of the target bounding box and classification score are not properly addressed by most existing trackers, resulting in inaccurate tracking performance. In this paper, we propose to learn bounding box distribution in training and calibrate the center point response in inference for robust online tracking. Specifically, we propose a simple yet effective bounding box distribution learning (BDL) module to model the target bounding box distribution and enhance the localization ability of our network. Furthermore, we propose a center point calibration (CPC) module to calibrate the origin classification score with the predicted localization uncertainty and generate an accurate target center point. The proposed tracking method is referred to as DLPC. The experimental results on four challenging datasets (i.e., OTB100, VOT2019, LaSOT, and TrackingNet) show that DLPC performs favorably against several state-of-the-art trackers while running in real-time at 60 fps.