Article

Segmenting people in small groups

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Larry S. DavisAuthors Info & Claims

VSSN '06: Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks

Pages 1 - 2

https://doi.org/10.1145/1178782.1178783

Published: 27 October 2006 Publication History

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Abstract

In this paper we review prior work on the problem of segmenting small groups of people into individuals in surveillance video.

References

[1]

Elgammal, A., and Davis, L.S., Probabilistic framework for segmenting people under occlusion, International Conference on Computer Vision, 2001, 145--152.

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[2]

Mittal, A., and Davis, L.S., M2 Tracker: A multi-view approach to segmenting and tracking people in a cluttered scene, International Journal of Computer Vision, 2005

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[3]

Kim, K, and Davis, L. S., Multi-camera tracking and segmentation of occluded people on ground plane using search-guided particle filtering, European Conference on Computer Vision, May 2006.

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[4]

Weiming Hu, Min Hu, Xue Zhou, Tieniu Tan and S.J. Maybank 2006 Principal axis based correspondence between multiple cameras for people tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 663--67.

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Index Terms

Segmenting people in small groups
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections

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Published In

VSSN '06: Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks

October 2006

230 pages

ISBN:1595934960

DOI:10.1145/1178782

General Chairs:
Jake K. Aggarwal
University of Texas
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chair:
Andrea Prati
University of Modena and Reggio Emilia, Italy

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2006

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MM06

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MM06: The 14th ACM International Conference on Multimedia 2006

October 27, 2006

California, Santa Barbara, USA

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People Detection under Occlusion in Multiple Camera Views

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