Affiliation:
1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, P. R. China
2. Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing, P. R. China
3. Yijiahe Technology Co. Ltd., Nanjing, P. R. China
Abstract
Kernelized Correlation Filters (KCF) for visual tracking have received much attention due to their fast speed and outstanding performances in real scenarios. However, the KCF sometimes still fails to track the targets with different scales, and it may drift because the target response is fixed and the original histogram of orientation gradient (HOG) features cannot represent the targets well. In this paper, we propose a novel fast tracker, which is based on KCF and insensitive to scale changes by learning two independent correlation filters (CFs) where one filter is designed for position estimation and the other is for scale estimation. In addition, it can adaptively change the target response and multiple features are integrated to improve the performance for our tracker. Finally, we employ an adaptive high confidence filters updating scheme to avoid errors. Evaluated on the popular OTB50 and OTB100 datasets, our proposed trackers show superior performances in terms of efficiency and accuracy compared to the existing methods.
Funder
Basic Research Program of Jiangsu Province
Fundamental Research Funds for the Central Universities
Postdoctoral Research Foundation of China
National Key Research and Development Program of China under Grant
Jiangsu Key Laboratory of Spectral Imaging and Intelligence Sense
Nanjing Key Technologies Breakthrough Project in Advantageous Industrials
Jiangsu Provincial Key Research and Development Program
Publisher
World Scientific Pub Co Pte Lt
Subject
Electrical and Electronic Engineering,Hardware and Architecture,Electrical and Electronic Engineering,Hardware and Architecture
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献