Visual Tracking with Bounding-Box Fine Adjustment

Abstract

Visual tracking is a fundamental topic in computer vision and its core issue is how to accurately and efficiently locate the object in challenging scenarios. In online tracking, improving the target positioning accuracy every frame and reducing the accumulation of tracking errors are the essential goals to obtaining robust performance. Convolutional Neural Network (CNN) based trackers have shown favorable performance gains in the problem of visual tracking in recent benchmarks. In this paper, we propose a novel bounding box adjustment module for online visual tracking. By using a lightweight Siamese neural network cascading behind a universal tracker, the confidence of the target region on two dimensions can be estimated refer to the reference image, which allow accurate adjustment of the object bounding box. Experiments are performed on OTB-2013 challenging benchmark tracking datasets and the results achieves improvement.