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

Authors:
Hejin Yuan;Yanning Zhang;Tao Zhou;Fang'An Deng;Xiuxiu Li;Huiling Lu
Affiliations:
School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China;School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China;School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China and Department of Maths, Shanxi University of Technology, Hanzhong, Shanxi 723000, China;Department of Maths, Shanxi University of Technology, Hanzhong, Shanxi 723000, China;School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China;Department of Computer, Shanxi University of Technology, Hanzhong , Shanxi 723000, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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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.