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Moving Object Detection in Compressed Domain for High Resolution Videos

Published: 07 December 2017 Publication History

Abstract

Motion detection in pixel domain of videos often requires highly capable resource to perform. This is due to computational demands for decoding the videos to obtain visual image. The problem can be solved by performing motion detection directly on bit streams of the compressed video data. This paper presents a new method for moving object detection in compressed domain of high resolution surveillance videos. In this work, we use video coding parameters (eg. motion vectors) extracted directly from bit stream of the compressed video to separate the moving blocks out of video background. We then propose new techniques for the segmentation and refinement of the foreground blocks to detect the moving objects. Experiments have been conducted on both public and self-recorded video data sets. Experimental results show the outperformance of our proposed method in comparing with conventional one.

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Cited By

View all
  • (2023)Design of Dynamic Target Tracking System Based on FPGADynamic Target Tracking SystemProceedings of the 2023 International Conference on Artificial Intelligence, Systems and Network Security10.1145/3661638.3661689(258-262)Online publication date: 22-Dec-2023
  • (2022)A Novel User-Friendly Application for Foreground Detection with Post-Processing in Surveillance Video AnalyticsInternational Journal of Electrical and Electronics Research10.37391/ijeer.10047710:4(1256-1261)Online publication date: 30-Dec-2022
  • (2022)Object detection methods on compressed domain videos: An overview, comparative analysis, and new directionsMeasurement10.1016/j.measurement.2022.112371(112371)Online publication date: Dec-2022

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Published In

cover image ACM Other conferences
SoICT '17: Proceedings of the 8th International Symposium on Information and Communication Technology
December 2017
486 pages
ISBN:9781450353281
DOI:10.1145/3155133
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • SOICT: School of Information and Communication Technology - HUST
  • NAFOSTED: The National Foundation for Science and Technology Development

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 December 2017

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Author Tags

  1. Compressed video
  2. Motion detection
  3. Object segmentation

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  • Research-article
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  • Refereed limited

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SoICT 2017

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Overall Acceptance Rate 147 of 318 submissions, 46%

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Cited By

View all
  • (2023)Design of Dynamic Target Tracking System Based on FPGADynamic Target Tracking SystemProceedings of the 2023 International Conference on Artificial Intelligence, Systems and Network Security10.1145/3661638.3661689(258-262)Online publication date: 22-Dec-2023
  • (2022)A Novel User-Friendly Application for Foreground Detection with Post-Processing in Surveillance Video AnalyticsInternational Journal of Electrical and Electronics Research10.37391/ijeer.10047710:4(1256-1261)Online publication date: 30-Dec-2022
  • (2022)Object detection methods on compressed domain videos: An overview, comparative analysis, and new directionsMeasurement10.1016/j.measurement.2022.112371(112371)Online publication date: Dec-2022

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