Next Article in Journal
Forecasting Issues of Wireless Communication Networks’ Cyber Resilience for An Intelligent Transportation System: An Overview of Cyber Attacks
Previous Article in Journal
Identifying a Medical Department Based on Unstructured Data: A Big Data Application in Healthcare
Article Menu

Export Article

Open AccessArticle
Information 2019, 10(1), 26; https://doi.org/10.3390/info10010026

Visual Object Tracking Robust to Illumination Variation Based on Hyperline Clustering

1
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
2
School of Physics and Electromechanical Engineering, Shaoguan University, Shaoguan 512026, China
*
Author to whom correspondence should be addressed.
Received: 7 December 2018 / Revised: 4 January 2019 / Accepted: 10 January 2019 / Published: 14 January 2019
  |  
PDF [2626 KB, uploaded 14 January 2019]
  |  

Abstract

Color histogram-based trackers have obtained excellent performance against many challenging situations. However, since the appearance of color is sensitive to illumination, they tend to achieve lower accuracy when illumination is severely variant throughout a sequence. To overcome this limitation, we propose a novel hyperline clustering based discriminant model, an illumination invariant model that is able to distinguish the object from its surrounding background. Furthermore, we exploit this model and propose an anchor based scale estimation to cope with shape deformation and scale variation. Numerous experiments on recent online tracking benchmark datasets demonstrate that our approach achieve favorable performance compared with several state-of-the-art tracking algorithms. In particular, our approach achieves higher accuracy than comparative methods in the illumination variant and shape deformation challenging situations. View Full-Text
Keywords: visual tracking; hyperline clustering; illumination variation; discriminant model; scale estimation visual tracking; hyperline clustering; illumination variation; discriminant model; scale estimation
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Yang, S.; Xie, Y.; Li, P.; Wen, H.; Luo, H.; He, Z. Visual Object Tracking Robust to Illumination Variation Based on Hyperline Clustering. Information 2019, 10, 26.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top