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Background independent moving object segmentation using edge similarity measure

Background independent moving object segmentation using edge similarity measure
Background independent moving object segmentation using edge similarity measure

Background modeling is one of the most challenging and time consuming tasks in moving object detection for video surveillance. In this paper, we present a new algorithm which does not require any background model. Instead, it utilizes three most recent consecutive frames to detect the presence of moving object by extracting moving edges. In the proposed method, we introduce an edge segment based approach instead of traditional edge pixel based approach. We also utilize an efficient edge-matching algorithm which reduces the variation of edge localization in different frames. Finally, regions of the moving objects are extracted from previously detected moving edges by using an efficient watershed based segmentation algorithm. The proposed method is characterized through robustness against the random noise, illumination variations and quantization error and is validated with the extensive experimental results.

0302-9743
318-329
Springer
Dewan, M. Ali Akber
363af584-4a43-4a28-8f3c-25b78fbd5360
Hossain, M. Julius
bba1b875-7604-462b-a55b-ba0b54f728e8
Chae, Oksam
65c96d9d-fb4d-4e41-a009-bc829dc7757c
Dewan, M. Ali Akber
363af584-4a43-4a28-8f3c-25b78fbd5360
Hossain, M. Julius
bba1b875-7604-462b-a55b-ba0b54f728e8
Chae, Oksam
65c96d9d-fb4d-4e41-a009-bc829dc7757c

Dewan, M. Ali Akber, Hossain, M. Julius and Chae, Oksam (2007) Background independent moving object segmentation using edge similarity measure. In Image Analysis and Recognition : 4th International Conference, ICIAR 2007, Proceedings. vol. 4633 LNCS, Springer. pp. 318-329 . (doi:10.1007/978-3-540-74260-9_29).

Record type: Conference or Workshop Item (Paper)

Abstract

Background modeling is one of the most challenging and time consuming tasks in moving object detection for video surveillance. In this paper, we present a new algorithm which does not require any background model. Instead, it utilizes three most recent consecutive frames to detect the presence of moving object by extracting moving edges. In the proposed method, we introduce an edge segment based approach instead of traditional edge pixel based approach. We also utilize an efficient edge-matching algorithm which reduces the variation of edge localization in different frames. Finally, regions of the moving objects are extracted from previously detected moving edges by using an efficient watershed based segmentation algorithm. The proposed method is characterized through robustness against the random noise, illumination variations and quantization error and is validated with the extensive experimental results.

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More information

Published date: 2007
Venue - Dates: 4th International Conference on Image Analysis and Recognition, ICIAR 2007, , Montreal, Canada, 2007-08-22 - 2007-08-24

Identifiers

Local EPrints ID: 469943
URI: http://eprints.soton.ac.uk/id/eprint/469943
ISSN: 0302-9743
PURE UUID: 48ee49af-4fc0-4662-b481-29584ae0660b
ORCID for M. Julius Hossain: ORCID iD orcid.org/0000-0003-3303-5755

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Date deposited: 29 Sep 2022 16:34
Last modified: 06 Jun 2024 02:13

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Contributors

Author: M. Ali Akber Dewan
Author: M. Julius Hossain ORCID iD
Author: Oksam Chae

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