A Multi-stage Competitive Neural Networks Approach for Motion Trajectory Pattern Learning View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2007

AUTHORS

Hejin Yuan , Yanning Zhang , Tao Zhou , Fang’an Deng , Xiuxiu Li , Huiling Lu

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. More... »

PAGES

796-803

Book

TITLE

Advances in Neural Networks – ISNN 2007

ISBN

978-3-540-72382-0
978-3-540-72383-7

From Grant

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-72383-7_93

DOI

http://dx.doi.org/10.1007/978-3-540-72383-7_93

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1013207587


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