Next Article in Journal
Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People
Previous Article in Journal
Intra-Tissue Pressure Measurement in Ex Vivo Liver Undergoing Laser Ablation with Fiber-Optic Fabry-Perot Probe
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(4), 545; doi:10.3390/s16040545

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update

1
National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China
2
Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 68503, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 18 January 2016 / Revised: 11 April 2016 / Accepted: 11 April 2016 / Published: 15 April 2016
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [5752 KB, uploaded 15 April 2016]   |  

Abstract

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm. View Full-Text
Keywords: visual tracking; exemplar-based detection; convolutional neural network (CNN) features; Gaussian mixture model visual tracking; exemplar-based detection; convolutional neural network (CNN) features; Gaussian mixture model
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

Gao, C.; Shi, H.; Yu, J.-G.; Sang, N. Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update. Sensors 2016, 16, 545.

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