Next Article in Journal
Combining Remote Temperature Sensing with in-Situ Sensing to Track Marine/Freshwater Mixing Dynamics
Next Article in Special Issue
Real-Time Robust Tracking for Motion Blur and Fast Motion via Correlation Filters
Previous Article in Journal
Empirical Study on Designing of Gaze Tracking Camera Based on the Information of User’s Head Movement
Previous Article in Special Issue
A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(9), 1406; doi:10.3390/s16091406

Onboard Robust Visual Tracking for UAVs Using a Reliable Global-Local Object Model

1
School of Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), 50 Nanyang Avenue, Singapore 639798, Singapore
2
ST Engineering-NTU Corporate Laboratory, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Gonzalez Toro
Received: 13 July 2016 / Revised: 24 August 2016 / Accepted: 25 August 2016 / Published: 31 August 2016
(This article belongs to the Special Issue UAV-Based Remote Sensing)

Abstract

In this paper, we present a novel onboard robust visual algorithm for long-term arbitrary 2D and 3D object tracking using a reliable global-local object model for unmanned aerial vehicle (UAV) applications, e.g., autonomous tracking and chasing a moving target. The first main approach in this novel algorithm is the use of a global matching and local tracking approach. In other words, the algorithm initially finds feature correspondences in a way that an improved binary descriptor is developed for global feature matching and an iterative Lucas–Kanade optical flow algorithm is employed for local feature tracking. The second main module is the use of an efficient local geometric filter (LGF), which handles outlier feature correspondences based on a new forward-backward pairwise dissimilarity measure, thereby maintaining pairwise geometric consistency. In the proposed LGF module, a hierarchical agglomerative clustering, i.e., bottom-up aggregation, is applied using an effective single-link method. The third proposed module is a heuristic local outlier factor (to the best of our knowledge, it is utilized for the first time to deal with outlier features in a visual tracking application), which further maximizes the representation of the target object in which we formulate outlier feature detection as a binary classification problem with the output features of the LGF module. Extensive UAV flight experiments show that the proposed visual tracker achieves real-time frame rates of more than thirty-five frames per second on an i7 processor with 640 × 512 image resolution and outperforms the most popular state-of-the-art trackers favorably in terms of robustness, efficiency and accuracy. View Full-Text
Keywords: unmanned aerial vehicle; visual object tracking; reliable global-local model; local geometric filter; local outlier factor; robust real-time performance unmanned aerial vehicle; visual object tracking; reliable global-local model; local geometric filter; local outlier factor; robust real-time performance
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).

Supplementary material

  • Externally hosted supplementary file 1
    Link: https://youtu.be/cu9cUYqJ1P8
    Description: The link shows the new video related to tracking results in all image sequences

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

Fu, C.; Duan, R.; Kircali, D.; Kayacan, E. Onboard Robust Visual Tracking for UAVs Using a Reliable Global-Local Object Model. Sensors 2016, 16, 1406.

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