research-article

A Unified Approach to Moving Object Detection in 2D and 3D Scenes

Authors:

P. AnandanAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 20, Issue 6

Pages 577 - 589

https://doi.org/10.1109/34.683770

Published: 01 June 1998 Publication History

Abstract

The detection of moving objects is important in many tasks. Previous approaches to this problem can be broadly divided into two classes: 2D algorithms which apply when the scene can be approximated by a flat surface and/or when the camera is only undergoing rotations and zooms, and 3D algorithms which work well only when significant depth variations are present in the scene and the camera is translating. In this paper, we describe a unified approach to handling moving-object detection in both 2D and 3D scenes, with a strategy to gracefully bridge the gap between those two extremes. Our approach is based on a stratification of the moving object-detection problem into scenarios which gradually increase in their complexity. We present a set of techniques that match the above stratification. These techniques progressively increase in their complexity, ranging from 2D techniques to more complex 3D techniques. Moreover, the computations required for the solution to the problem at one complexity level become the initial processing step for the solution at the next complexity level. We illustrate these techniques using examples from real-image sequences.

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Cited By

Bideau PLearned-Miller ESchmid CAlahari K(2024)The Right Spin: Learning Object Motion from Rotation-Compensated Flow FieldsInternational Journal of Computer Vision10.1007/s11263-023-01859-x132:1(40-55)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11263-023-01859-x
Zhou TPorikli FCrandall DVan Gool LWang W(2023)A Survey on Deep Learning Technique for Video SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.322557345:6(7099-7122)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TPAMI.2022.3225573
Meunier EBadoual ABouthemy P(2023)EM-Driven Unsupervised Learning for Efficient Motion SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.319848045:4(4462-4473)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TPAMI.2022.3198480
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Index Terms

A Unified Approach to Moving Object Detection in 2D and 3D Scenes
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
        Motion capture
  2. Computer graphics
    1. Animation
      1. Motion capture
      2. Motion processing
    2. Shape modeling
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Computational geometry

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Information

Published In

cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 20, Issue 6

June 1998

95 pages

ISSN:0162-8828

Editor:
Rangachar Kasturi
Pennsylvania State Univ., Univ. Park

Issue’s Table of Contents

Copyright © Copyright © 1998 IEEE. All Rights Reserved.

Publisher

IEEE Computer Society

United States

Publication History

Published: 01 June 1998

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Cited By

Bideau PLearned-Miller ESchmid CAlahari K(2024)The Right Spin: Learning Object Motion from Rotation-Compensated Flow FieldsInternational Journal of Computer Vision10.1007/s11263-023-01859-x132:1(40-55)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11263-023-01859-x
Zhou TPorikli FCrandall DVan Gool LWang W(2023)A Survey on Deep Learning Technique for Video SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.322557345:6(7099-7122)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TPAMI.2022.3225573
Meunier EBadoual ABouthemy P(2023)EM-Driven Unsupervised Learning for Efficient Motion SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.319848045:4(4462-4473)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TPAMI.2022.3198480
Xu BWu QChai ZGuo XShi J(2023)GAMAComputer Vision and Image Understanding10.1016/j.cviu.2023.103751234:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.cviu.2023.103751
Yao RLin GXia SZhao JZhou Y(2020)Video Object Segmentation and TrackingACM Transactions on Intelligent Systems and Technology10.1145/339174311:4(1-47)Online publication date: 25-May-2020
https://dl.acm.org/doi/10.1145/3391743
Chapel MBouwmans T(2020)Moving objects detection with a moving cameraComputer Science Review10.1016/j.cosrev.2020.10031038:COnline publication date: 1-Nov-2020
https://dl.acm.org/doi/10.1016/j.cosrev.2020.100310
Sajid HCheung SJacobs N(2019)Motion and appearance based background subtraction for freely moving camerasImage Communication10.1016/j.image.2019.03.00375:C(11-21)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1016/j.image.2019.03.003
Lezki HOzturk IAkpinar MYucel MLogoglu KErdem AErdem E(2018)Joint Exploitation of Features and Optical Flow for Real-Time Moving Object Detection on DronesComputer Vision – ECCV 2018 Workshops10.1007/978-3-030-11012-3_8(100-116)Online publication date: 8-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-11012-3_8
Hu YHuang JSchwing A(2017)MaskRNNProceedings of the 31st International Conference on Neural Information Processing Systems10.5555/3294771.3294802(324-333)Online publication date: 4-Dec-2017
https://dl.acm.org/doi/10.5555/3294771.3294802
Wu YHe XNguyen T(2017)Moving Object Detection With a Freely Moving Camera via Background Motion SubtractionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2015.249349927:2(236-248)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1109/TCSVT.2015.2493499
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