Next Article in Journal
Improved Spatial Differencing Scheme for 2-D DOA Estimation of Coherent Signals with Uniform Rectangular Arrays
Next Article in Special Issue
American Sign Language Alphabet Recognition Using a Neuromorphic Sensor and an Artificial Neural Network
Previous Article in Journal
A Behaviour Monitoring System (BMS) for Ambient Assisted Living
Previous Article in Special Issue
Headgear Accessories Classification Using an Overhead Depth Sensor
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(9), 1945; doi:10.3390/s17091945

Comparative Evaluation of Background Subtraction Algorithms in Remote Scene Videos Captured by MWIR Sensors

1
Institute of Optics and Electronics, Chinese Academy of Sciences, P.O. Box 350, Shuangliu, Chengdu 610209, China
2
School of Optoelectronic Information, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu 610054, China
3
University of Chinese Academy of Sciences, 19 A Yuquan Rd, Shijingshan District, Beijing 100039, China
4
China Huayin Ordnance Test Center, Huayin 714200, China
*
Author to whom correspondence should be addressed.
Received: 1 July 2017 / Revised: 7 August 2017 / Accepted: 17 August 2017 / Published: 24 August 2017
(This article belongs to the Special Issue Video Analysis and Tracking Using State-of-the-Art Sensors)
View Full-Text   |   Download PDF [2308 KB, uploaded 26 August 2017]   |  

Abstract

Background subtraction (BS) is one of the most commonly encountered tasks in video analysis and tracking systems. It distinguishes the foreground (moving objects) from the video sequences captured by static imaging sensors. Background subtraction in remote scene infrared (IR) video is important and common to lots of fields. This paper provides a Remote Scene IR Dataset captured by our designed medium-wave infrared (MWIR) sensor. Each video sequence in this dataset is identified with specific BS challenges and the pixel-wise ground truth of foreground (FG) for each frame is also provided. A series of experiments were conducted to evaluate BS algorithms on this proposed dataset. The overall performance of BS algorithms and the processor/memory requirements were compared. Proper evaluation metrics or criteria were employed to evaluate the capability of each BS algorithm to handle different kinds of BS challenges represented in this dataset. The results and conclusions in this paper provide valid references to develop new BS algorithm for remote scene IR video sequence, and some of them are not only limited to remote scene or IR video sequence but also generic for background subtraction. The Remote Scene IR dataset and the foreground masks detected by each evaluated BS algorithm are available online: https://github.com/JerryYaoGl/BSEvaluationRemoteSceneIR. View Full-Text
Keywords: background subtraction; remote scene; IR video sequence; MWIR sensor; background modeling; foreground detection background subtraction; remote scene; IR video sequence; MWIR sensor; background modeling; foreground detection
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

Yao, G.; Lei, T.; Zhong, J.; Jiang, P.; Jia, W. Comparative Evaluation of Background Subtraction Algorithms in Remote Scene Videos Captured by MWIR Sensors. Sensors 2017, 17, 1945.

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