Next Article in Journal
A New Low Complexity Angle of Arrival Algorithm for 1D and 2D Direction Estimation in MIMO Smart Antenna Systems
Next Article in Special Issue
Vision System for Coarsely Estimating Motion Parameters for Unknown Fast Moving Objects in Space
Previous Article in Journal
Biosensors to Diagnose Chagas Disease: A Brief Review
Previous Article in Special Issue
Motion-Blur-Free High-Speed Video Shooting Using a Resonant Mirror
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(11), 2626; doi:10.3390/s17112626

Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters

Department of Information and Communication Engineering, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Author to whom correspondence should be addressed.
Received: 27 September 2017 / Revised: 9 November 2017 / Accepted: 11 November 2017 / Published: 15 November 2017
(This article belongs to the Special Issue Video Analysis and Tracking Using State-of-the-Art Sensors)


In recent years, visual object tracking has been widely used in military guidance, human-computer interaction, road traffic, scene monitoring and many other fields. The tracking algorithms based on correlation filters have shown good performance in terms of accuracy and tracking speed. However, their performance is not satisfactory in scenes with scale variation, deformation, and occlusion. In this paper, we propose a scene-aware adaptive updating mechanism for visual tracking via a kernel correlation filter (KCF). First, a low complexity scale estimation method is presented, in which the corresponding weight in five scales is employed to determine the final target scale. Then, the adaptive updating mechanism is presented based on the scene-classification. We classify the video scenes as four categories by video content analysis. According to the target scene, we exploit the adaptive updating mechanism to update the kernel correlation filter to improve the robustness of the tracker, especially in scenes with scale variation, deformation, and occlusion. We evaluate our tracker on the CVPR2013 benchmark. The experimental results obtained with the proposed algorithm are improved by 33.3%, 15%, 6%, 21.9% and 19.8% compared to those of the KCF tracker on the scene with scale variation, partial or long-time large-area occlusion, deformation, fast motion and out-of-view. View Full-Text
Keywords: visual object tracking; scene-classification; adaptive updating mechanism; occlusion visual object tracking; scene-classification; adaptive updating mechanism; occlusion

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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Li, F.; Zhang, S.; Qiao, X. Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters. Sensors 2017, 17, 2626.

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



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top