Robust visual tracking based on response stability

Abstract

In this paper, a new approach of response stability based for visual object tracking is developed. This approach proposes a response stability criterion to measure the tracking quality and fuse tracking results of multiple layers of a convolutional neural network (CNN). Inspired by recent detection based methods for visual tracking, the detection capability of EdgeBoxes is investigated, and proposes to re-detect target when tracking failure occurs. In addition, 3D locally adaptive regression kernels (LARK) feature is employed in correlation filter based tracking framework. Extensive experimental results and performance compared with state-of-the-art tracking algorithms on challenging benchmark datasets show that our method is more accurate and robust.