Home > Research > Publications & Outputs > Video Object Motion Segmentation for Intelligen...

Research

Links

Text available via DOI:

View graph of relations

Video Object Motion Segmentation for Intelligent Visual Surveillance

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

Standard

Video Object Motion Segmentation for Intelligent Visual Surveillance. / Jiang, M.; Crookes, D.
International Machine Vision and Image Processing Conference (IMVIP 2007). IEEE, 2007. p. 202.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Jiang, M & Crookes, D 2007, Video Object Motion Segmentation for Intelligent Visual Surveillance. in International Machine Vision and Image Processing Conference (IMVIP 2007). IEEE, pp. 202. https://doi.org/10.1109/imvip.2007.7

APA

Jiang, M., & Crookes, D. (2007). Video Object Motion Segmentation for Intelligent Visual Surveillance. In International Machine Vision and Image Processing Conference (IMVIP 2007) (pp. 202). IEEE. https://doi.org/10.1109/imvip.2007.7

Vancouver

Jiang M, Crookes D. Video Object Motion Segmentation for Intelligent Visual Surveillance. In International Machine Vision and Image Processing Conference (IMVIP 2007). IEEE. 2007. p. 202 doi: 10.1109/imvip.2007.7

Author

Jiang, M. ; Crookes, D. / Video Object Motion Segmentation for Intelligent Visual Surveillance. International Machine Vision and Image Processing Conference (IMVIP 2007). IEEE, 2007. pp. 202

Bibtex

@inproceedings{32a006a0f7644e62ba110728738708f0,
title = "Video Object Motion Segmentation for Intelligent Visual Surveillance",
abstract = "This paper presents a video object motion segmentation method for object tracking in visual surveillance. In the first step, the frames are first decomposed into small facets (regions), using colour information. Then, based on the detected motion, the motion segmentation is performed at facet level. A Bayesian approach is applied in clustering facets into moving objects and tracking moving video objects. Experiments have verified that the proposed method can efficiently tackle the complexity of video motion tracking.",
author = "M. Jiang and D. Crookes",
year = "2007",
month = sep,
doi = "10.1109/imvip.2007.7",
language = "English",
isbn = "0769528872",
pages = "202",
booktitle = "International Machine Vision and Image Processing Conference (IMVIP 2007)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Video Object Motion Segmentation for Intelligent Visual Surveillance

AU - Jiang, M.

AU - Crookes, D.

PY - 2007/9

Y1 - 2007/9

N2 - This paper presents a video object motion segmentation method for object tracking in visual surveillance. In the first step, the frames are first decomposed into small facets (regions), using colour information. Then, based on the detected motion, the motion segmentation is performed at facet level. A Bayesian approach is applied in clustering facets into moving objects and tracking moving video objects. Experiments have verified that the proposed method can efficiently tackle the complexity of video motion tracking.

AB - This paper presents a video object motion segmentation method for object tracking in visual surveillance. In the first step, the frames are first decomposed into small facets (regions), using colour information. Then, based on the detected motion, the motion segmentation is performed at facet level. A Bayesian approach is applied in clustering facets into moving objects and tracking moving video objects. Experiments have verified that the proposed method can efficiently tackle the complexity of video motion tracking.

U2 - 10.1109/imvip.2007.7

DO - 10.1109/imvip.2007.7

M3 - Conference contribution/Paper

SN - 0769528872

SN - 9780769528878

SP - 202

BT - International Machine Vision and Image Processing Conference (IMVIP 2007)

PB - IEEE

ER -