Next Article in Journal
Monitoring System Analysis for Evaluating a Building’s Envelope Energy Performance through Estimation of Its Heat Loss Coefficient
Next Article in Special Issue
Generalized Vision-Based Detection, Identification and Pose Estimation of Lamps for BIM Integration
Previous Article in Journal
A Review of Ion Implantation Technology for Image Sensors
Previous Article in Special Issue
Multi-Focus Image Fusion Method for Vision Sensor Systems via Dictionary Learning with Guided Filter
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(7), 2359; https://doi.org/10.3390/s18072359

Robust Visual Tracking Based on Adaptive Convolutional Features and Offline Siamese Tracker

Academy of Astronautics, Northwestern Polytechnical University, YouYi Street, Xi’an 710072, China
*
Author to whom correspondence should be addressed.
Received: 15 May 2018 / Revised: 17 July 2018 / Accepted: 18 July 2018 / Published: 20 July 2018
Full-Text   |   PDF [9848 KB, uploaded 20 July 2018]   |  

Abstract

Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. The existing spatially regularized discriminative correlation filter (SRDCF) method learns partial-target information or background information when experiencing rotation, out of view, and heavy occlusion. In order to reduce the computational complexity by creating a novel method to enhance tracking ability, we first introduce an adaptive dimensionality reduction technique to extract the features from the image, based on pre-trained VGG-Net. We then propose an adaptive model update to assign weights during an update procedure depending on the peak-to-sidelobe ratio. Finally, we combine the online SRDCF-based tracker with the offline Siamese tracker to accomplish long term tracking. Experimental results demonstrate that the proposed tracker has satisfactory performance in a wide range of challenging tracking scenarios. View Full-Text
Keywords: spatially regularized discriminative correlation filter (SRDCF)-based visual tracking; deep convolutional features; adaptive dimensionality reduction; adaptive model update; offline Siamese tracker spatially regularized discriminative correlation filter (SRDCF)-based visual tracking; deep convolutional features; adaptive dimensionality reduction; adaptive model update; offline Siamese tracker
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

Share & Cite This Article

MDPI and ACS Style

Zhang, X.; Wang, M. Robust Visual Tracking Based on Adaptive Convolutional Features and Offline Siamese Tracker. Sensors 2018, 18, 2359.

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