Next Article in Journal
Study of Intrinsic Dissipation Due to Thermoelastic Coupling in Gyroscope Resonators
Next Article in Special Issue
Spatial Ecology of Estuarine Crocodile (Crocodylus porosus) Nesting in a Fragmented Landscape
Previous Article in Journal
Pretreated Butterfly Wings for Tuning the Selective Vapor Sensing
Previous Article in Special Issue
Onboard Robust Visual Tracking for UAVs Using a Reliable Global-Local Object Model
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(9), 1443; doi:10.3390/s16091443

Real-Time Robust Tracking for Motion Blur and Fast Motion via Correlation Filters

1,2,3,* , 1,2
,
1,2
and
1,2
1
Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China
2
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3
University of Chinese Academy of Science, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Academic Editors: Felipe Gonzalez Toro and Antonios Tsourdos
Received: 22 June 2016 / Revised: 15 August 2016 / Accepted: 17 August 2016 / Published: 7 September 2016
(This article belongs to the Special Issue UAV-Based Remote Sensing)
View Full-Text   |   Download PDF [2061 KB, uploaded 7 September 2016]   |  

Abstract

Visual tracking has extensive applications in intelligent monitoring and guidance systems. Among state-of-the-art tracking algorithms, Correlation Filter methods perform favorably in robustness, accuracy and speed. However, it also has shortcomings when dealing with pervasive target scale variation, motion blur and fast motion. In this paper we proposed a new real-time robust scheme based on Kernelized Correlation Filter (KCF) to significantly improve performance on motion blur and fast motion. By fusing KCF and STC trackers, our algorithm also solve the estimation of scale variation in many scenarios. We theoretically analyze the problem for CFs towards motions and utilize the point sharpness function of the target patch to evaluate the motion state of target. Then we set up an efficient scheme to handle the motion and scale variation without much time consuming. Our algorithm preserves the properties of KCF besides the ability to handle special scenarios. In the end extensive experimental results on benchmark of VOT datasets show our algorithm performs advantageously competed with the top-rank trackers. View Full-Text
Keywords: visual tracking; motion blur; fast motion; correlation filter visual tracking; motion blur; fast motion; correlation filter
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 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

Xu, L.; Luo, H.; Hui, B.; Chang, Z. Real-Time Robust Tracking for Motion Blur and Fast Motion via Correlation Filters. Sensors 2016, 16, 1443.

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