Fast and Robust Object Tracking with Adaptive Detection

Abstract

Object detection and tracking is an important research topic in computer vision with numerous practical applications. Although great progress has been made both in object detection and tracking, it is still a big challenge in automatic real-time applications. In this paper, a fast and robust approach is proposed by integrating an adaptive object detection technique within a kernelized correlation filter (KCF) framework. The KCF tracker is automatically initialized via salient object detection and localization. An adaptive object detection strategy is proposed to refine the location and boundary of the object when the tracking confidence value is below a certain threshold. In addition, a reliable post-processing technique is designed to accurately localize the object from a saliency map. Extensive quantitative and qualitative experiments on the challenging datasets have been performed to verify the proposed approach, which also demonstrates that our approach greatly outperforms the stateof-the-art methods in terms of tracking speed and accuracy.