Next Article in Journal
Microengineered Conductive Elastomeric Electrodes for Long-Term Electrophysiological Measurements with Consistent Impedance under Stretch
Previous Article in Journal
Sensors for Highly Toxic Gases: Methylamine and Hydrogen Chloride Detection at Low Concentrations in an Ionic Liquid on Pt Screen Printed Electrodes
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(10), 26877-26905;

Visual Tracking Based on Extreme Learning Machine and Sparse Representation

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Beijing Key Laboratory of Embedded Real-Time Information Processing Technology, Beijing 100081, China
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 13 August 2015 / Revised: 15 October 2015 / Accepted: 16 October 2015 / Published: 22 October 2015
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [7036 KB, uploaded 22 October 2015]   |  


The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker. View Full-Text
Keywords: visual tracking; extreme learning machine; sparse representation; manifold learning; accelerated proximal gradient visual tracking; extreme learning machine; sparse representation; manifold learning; accelerated proximal gradient

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).

Share & Cite This Article

MDPI and ACS Style

Wang, B.; Tang, L.; Yang, J.; Zhao, B.; Wang, S. Visual Tracking Based on Extreme Learning Machine and Sparse Representation. Sensors 2015, 15, 26877-26905.

Show more citation formats Show less citations formats

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