Article

Foreground object detection from videos containing complex background

Authors:

Liyuan Li,

Weimin Huang,

Irene Y. H. Gu,

Qi TianAuthors Info & Claims

MULTIMEDIA '03: Proceedings of the eleventh ACM international conference on Multimedia

Pages 2 - 10

https://doi.org/10.1145/957013.957017

Published: 02 November 2003 Publication History

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Abstract

This paper proposes a novel method for detection and segmentation of foreground objects from a video which contains both stationary and moving background objects and undergoes both gradual and sudden "once-off" changes. A Bayes decision rule for classification of background and foreground from selected feature vectors is formulated. Under this rule, different types of background objects will be classified from foreground objects by choosing a proper feature vector. The stationary background object is described by the color feature, and the moving background object is represented by the color co-occurrence feature. Foreground objects are extracted by fusing the classification results from both stationary and moving pixels. Learning strategies for the gradual and sudden "once-off" background changes are proposed to adapt to various changes in background through the video. The convergence of the learning process is proved and a formula to select a proper learning rate is also derived. Experiments have shown promising results in extracting foreground objects from many complex backgrounds including wavering tree branches, flickering screens and water surfaces, moving escalators, opening and closing doors, switching lights and shadows of moving objects.

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Jan MGarbin CRuetschi JMarques OKalva H(2024)Automated patient localization in challenging hospital environmentsMultimedia Tools and Applications10.1007/s11042-024-18118-x83:23(63439-63457)Online publication date: 12-Jan-2024
https://doi.org/10.1007/s11042-024-18118-x
Praveen PMadihabanu S(2023)A Real Time Multiple Object Tracking in Videos using CNN Algorithm2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)10.1109/ICSSAS57918.2023.10331876(1-6)Online publication date: 18-Oct-2023
https://doi.org/10.1109/ICSSAS57918.2023.10331876
Gatenbee CBaker APrabhakaran SSwinyard OSlebos RMandal GMulholland EAndor NMarusyk ALeedham SConejo-Garcia JChung CRobertson-Tessi MGraham TAnderson A(2023)Virtual alignment of pathology image series for multi-gigapixel whole slide imagesNature Communications10.1038/s41467-023-40218-914:1Online publication date: 26-Jul-2023
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Index Terms

Foreground object detection from videos containing complex background
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation
      1. Image processing

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Published In

MULTIMEDIA '03: Proceedings of the eleventh ACM international conference on Multimedia

November 2003

670 pages

ISBN:1581137222

DOI:10.1145/957013

General Chairs:
Lawrence Rowe
University of California, Berkeley
,
Harrick Vin
University of Texas, Austin
,
Program Chairs:
Thomas Plagemann
University of Oslo
,
Prashant Shenoy
University of Massachusetts, Amherst
,
John R. Smith
IBM T.J. Watson Research Center

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]

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Association for Computing Machinery

New York, NY, United States

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Published: 02 November 2003

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MM03: 2003 11th Annual ACM International Conference on Multimedia

November 2 - 8, 2003

CA, Berkeley, USA

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Overall Acceptance Rate 995 of 4,171 submissions, 24%

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Cited By

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https://doi.org/10.1007/s11042-024-18118-x
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Gatenbee CBaker APrabhakaran SSwinyard OSlebos RMandal GMulholland EAndor NMarusyk ALeedham SConejo-Garcia JChung CRobertson-Tessi MGraham TAnderson A(2023)Virtual alignment of pathology image series for multi-gigapixel whole slide imagesNature Communications10.1038/s41467-023-40218-914:1Online publication date: 26-Jul-2023
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