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.
This work was supported by the National Natural Science Foundation of China (NSFC) under Grants 60472072, the Specialized Research Foundation for the Doctoral Program of Higher Education under Grant 20040699034 , Cthe Aeronautical Science Foundation of China under Grant 04I50370 and the Natural Science Foundation of Shan’xi Province.
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Yuan, H., Zhang, Y., Zhou, T., Deng, F., Li, X., Lu, H. (2007). A Multi-stage Competitive Neural Networks Approach for Motion Trajectory Pattern Learning. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_93
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DOI: https://doi.org/10.1007/978-3-540-72383-7_93
Publisher Name: Springer, Berlin, Heidelberg
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