[1] Yang Biao, Lin Guoyu, Zhang Weigong,. Adaptive topology learning of camera networkacross non-overlapping views [J]. Journal of Southeast University (English Edition), 2015, 31 (1): 61-66. [doi:10.3969/j.issn.1003-7985.2015.01.011]

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Adaptive topology learning of camera networkacross non-overlapping views()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

31

Issue:

2015 1

Page:

61-66

Research Field:

Computer Science and Engineering

Publishing date:

2015-03-30

Info

Title:

Adaptive topology learning of camera networkacross non-overlapping views

Author(s):

Yang Biao¹;  Lin Guoyu²;  Zhang Weigong²

¹Faculty of Information Science and Engineering, Changzhou University, Changzhou 213164, China
²School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China

Keywords:

non-overlapping views;  mutual information;  Gaussian mixture model;  adaptive topology learning;  cross-correlation function

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2015.01.011

Abstract:

An adaptive topology learning approach is proposed to learn the topology of a practical camera network in an unsupervised way. The nodes are modeled by the Gaussian mixture model. The connectivity between nodes is judged by their cross-correlation function, which is also used to calculate their transition time distribution. The mutual information of the connected node pair is employed for transition probability calculation. A false link eliminating approach is proposed, along with a topology updating strategy to improve the learned topology. A real monitoring system with five disjoint cameras is built for experiments. Comparative results with traditional methods show that the proposed method is more accurate in topology learning and is more robust to environmental changes.

References:

[1] Javed O, Rasheed Z, Shafique K, et al. Tracking across multiple cameras with disjoint views [C]//Proceedings of the Ninth IEEE International Conference on Computer Vision. Nice, France, 2003: 952-957.
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[5] Center for Research in Intelligent Systems. Continuous learning of a multilayered network topology in a video camera network[R]. Berkeley, CA, USA: University of California at Berkeley, 2009.
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[7] Piccardi M, Cheng E D. Tracking matching over disjoint camera views based on an incremental major color spectrum histogram [C]//IEEE Conference on Advanced Video and Signal Based Surveillance. Como, Italy, 2005: 147-152.
[8] Alahi A, Vandergheynst P, Bierlaire M, et al. Cascade of descriptors to detect and track objects across any network of cameras[J]. Computer Vision and Image Understanding, 2010, 114(6): 624-640.
[9] Zhao R, Ouyang W L, Wang X G. Unsupervised salience learning for person re-identification [C]//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR, USA, 2013: 3586-3593.
[10] Viola P, Wells W M III. Alignment by maximization of mutual information[J]. International Journal of Computer Vision, 1997, 24(2): 137-154.
[11] Liu S H, Lai S M, Zhang M J. A min-cost flow based algorithm for objects association of multiple non-overlapping cameras[J]. ACAT Automatica Sinica, 2010, 36(10): 1484-1489.
[12] Exner D, Bruns E, Kurz D. Fast and robust CAMShift tracking [C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA, 2010: 9-16.

Memo

Memo:

Biography: Yang Biao(1987—), male, doctor, lecturer, yb6864171@126.com.
Foundation items: The National Natural Science Foundation of China(No. 60972001), the Science and Technology Plan of Suzhou City(No.SS201223).
Citation: Yang Biao, Lin Guoyu, Zhang Weigong. Adaptive topology learning of camera network across non-overlapping views[J].Journal of Southeast University(English Edition), 2015, 31(1):61-66.[doi:10.3969/j.issn.1003-7985.2015.01.011]

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