research-article

A Hybrid Background Subtraction Method with Background and Foreground Candidates Detection

Authors:

Fan-Chieh Cheng,

Shih-Chia HuangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 7, Issue 1

Article No.: 7, Pages 1 - 14

https://doi.org/10.1145/2746409

Published: 01 October 2015 Publication History

Abstract

Background subtraction for motion detection is often used in video surveillance systems. However, difficulties in bootstrapping restrict its development. This article proposes a novel hybrid background subtraction technique to solve this problem. For performance improvement of background subtraction, the proposed technique not only quickly initializes the background model but also eliminates unnecessary regions containing only background pixels in the object detection process. Furthermore, an embodiment based on the proposed technique is also presented. Experimental results verify that the proposed technique allows for reduced execution time as well as improvement of performance as evaluated by Recall, Precision, F1, and Similarity metrics when used with state-of-the-art background subtraction methods.

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Mandal MVipparthi S(2022)An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research NeedsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.307788323:7(6101-6122)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1109/TITS.2021.3077883
Yang LDeng ZQiu WHao YLi LCui JLi C(2022)A Measurement Method of the Shortest Distance Between Ultrahigh Ships and Transmission Lines Based on Binocular VisionIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2022.321757871(1-12)Online publication date: 2022
https://doi.org/10.1109/TIM.2022.3217578
Shi ZXu ZWang T(2022)A method for detecting pedestrian height and distance based on monocular vision technologyMeasurement10.1016/j.measurement.2022.111418199(111418)Online publication date: Aug-2022
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Index Terms

A Hybrid Background Subtraction Method with Background and Foreground Candidates Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 7, Issue 1

October 2015

293 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2830012

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 October 2015

Accepted: 01 March 2015

Revised: 01 December 2014

Received: 01 April 2014

Published in TIST Volume 7, Issue 1

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Cited By

Mandal MVipparthi S(2022)An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research NeedsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.307788323:7(6101-6122)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1109/TITS.2021.3077883
Yang LDeng ZQiu WHao YLi LCui JLi C(2022)A Measurement Method of the Shortest Distance Between Ultrahigh Ships and Transmission Lines Based on Binocular VisionIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2022.321757871(1-12)Online publication date: 2022
https://doi.org/10.1109/TIM.2022.3217578
Shi ZXu ZWang T(2022)A method for detecting pedestrian height and distance based on monocular vision technologyMeasurement10.1016/j.measurement.2022.111418199(111418)Online publication date: Aug-2022
https://doi.org/10.1016/j.measurement.2022.111418
Kurniawan Amiruddin MIndrabayu Areni IBustamin A(2021)An Approach for Vehicle’s Classification Using BRISK Feature Extraction2021 3rd International Conference on Electronics Representation and Algorithm (ICERA)10.1109/ICERA53111.2021.9538701(83-88)Online publication date: 29-Jul-2021
https://doi.org/10.1109/ICERA53111.2021.9538701
Saluky SSupangkat SNugraha I(2020)Abandoned Object Detection Method Using Convolutional Neural Network2020 International Conference on ICT for Smart Society (ICISS)10.1109/ICISS50791.2020.9307547(1-4)Online publication date: 19-Nov-2020
https://doi.org/10.1109/ICISS50791.2020.9307547
Masud UJeribi FAlhameed MTahir AJavaid QAkram F(2020)Traffic Congestion Avoidance System Using Foreground Estimation and Cascade ClassifierIEEE Access10.1109/ACCESS.2020.30277158(178859-178869)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3027715
B. STom AGeorge S(2019)Simultaneous denoising and moving object detection using low rank approximationFuture Generation Computer Systems10.1016/j.future.2018.07.06590(198-210)Online publication date: Jan-2019
https://doi.org/10.1016/j.future.2018.07.065
Velazquez-Pupo RSierra-Romero ATorres-Roman DShkvarko YSantiago-Paz JGómez-Gutiérrez DRobles-Valdez DHermosillo-Reynoso FRomero-Delgado M(2018)Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based SystemSensors10.3390/s1802037418:2(374)Online publication date: 27-Jan-2018
https://doi.org/10.3390/s18020374
Oh SKang H(2017)Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle SystemsSensors10.3390/s1701020717:12(207)Online publication date: 22-Jan-2017
https://doi.org/10.3390/s17010207
Zhang HLu F(2017)GSMNet: A Hierarchical Graph Model for Moving Objects in NetworksISPRS International Journal of Geo-Information10.3390/ijgi60300716:3(71)Online publication date: 3-Mar-2017
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