A multi-stage competitive neural networks approach for motion trajectory pattern learning

Hejin Yuan; Yanning Zhang; Tao Zhou; Fan G.An Deng; Xiuxiu Li; Huiling Lu

doi:10.1007/978-3-540-72383-7_93

A multi-stage competitive neural networks approach for motion trajectory pattern learning

Hejin Yuan, Yanning Zhang, Tao Zhou, Fan G.An Deng, Xiuxiu Li, Huiling Lu

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper puts forward a multi-stages competitive neural networks approach for motion trajectory pattern analysis and learning. In this method, the rival penalized competitive learning method, which could well overcome the competitive networks' problems of the selection of output neurons number and weight initialization, is used to discover the distribution of the flow vectors according to the trajectories' time orders. The experiments on different sites with CCD and infrared cameras demonstrate that our method is valid for motion trajectory pattern learning and can be used for anomaly detection in outdoor scenes.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings
Publisher	Springer Verlag
Pages	796-803
Number of pages	8
Edition	PART 1
ISBN (Print)	9783540723820
DOIs	https://doi.org/10.1007/978-3-540-72383-7_93
State	Published - 2007
Event	4th International Symposium on Neural Networks, ISNN 2007 - Nanjing, China Duration: 3 Jun 2007 → 7 Jun 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	4491 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Symposium on Neural Networks, ISNN 2007
Country/Territory	China
City	Nanjing
Period	3/06/07 → 7/06/07

Access to Document

10.1007/978-3-540-72383-7_93

Cite this

Yuan, H., Zhang, Y., Zhou, T., Deng, F. G. A., Li, X., & Lu, H. (2007). A multi-stage competitive neural networks approach for motion trajectory pattern learning. In Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings (PART 1 ed., pp. 796-803). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4491 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-540-72383-7_93

Yuan, Hejin ; Zhang, Yanning ; Zhou, Tao et al. / A multi-stage competitive neural networks approach for motion trajectory pattern learning. Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings. PART 1. ed. Springer Verlag, 2007. pp. 796-803 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

@inproceedings{8cd6c29c69fe45f888865d419b428221,

title = "A multi-stage competitive neural networks approach for motion trajectory pattern learning",

abstract = "This paper puts forward a multi-stages competitive neural networks approach for motion trajectory pattern analysis and learning. In this method, the rival penalized competitive learning method, which could well overcome the competitive networks' problems of the selection of output neurons number and weight initialization, is used to discover the distribution of the flow vectors according to the trajectories' time orders. The experiments on different sites with CCD and infrared cameras demonstrate that our method is valid for motion trajectory pattern learning and can be used for anomaly detection in outdoor scenes.",

author = "Hejin Yuan and Yanning Zhang and Tao Zhou and Deng, {Fan G.An} and Xiuxiu Li and Huiling Lu",

year = "2007",

doi = "10.1007/978-3-540-72383-7_93",

language = "英语",

isbn = "9783540723820",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "796--803",

booktitle = "Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings",

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note = "4th International Symposium on Neural Networks, ISNN 2007 ; Conference date: 03-06-2007 Through 07-06-2007",

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Yuan, H, Zhang, Y, Zhou, T, Deng, FGA, Li, X & Lu, H 2007, A multi-stage competitive neural networks approach for motion trajectory pattern learning. in Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 4491 LNCS, Springer Verlag, pp. 796-803, 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, 3/06/07. https://doi.org/10.1007/978-3-540-72383-7_93

A multi-stage competitive neural networks approach for motion trajectory pattern learning. / Yuan, Hejin; Zhang, Yanning; Zhou, Tao et al.
Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings. PART 1. ed. Springer Verlag, 2007. p. 796-803 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4491 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper puts forward a multi-stages competitive neural networks approach for motion trajectory pattern analysis and learning. In this method, the rival penalized competitive learning method, which could well overcome the competitive networks' problems of the selection of output neurons number and weight initialization, is used to discover the distribution of the flow vectors according to the trajectories' time orders. The experiments on different sites with CCD and infrared cameras demonstrate that our method is valid for motion trajectory pattern learning and can be used for anomaly detection in outdoor scenes.

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Yuan H, Zhang Y, Zhou T, Deng FGA, Li X, Lu H. A multi-stage competitive neural networks approach for motion trajectory pattern learning. In Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings. PART 1 ed. Springer Verlag. 2007. p. 796-803. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-540-72383-7_93

A multi-stage competitive neural networks approach for motion trajectory pattern learning

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