A kemelized Probabilistic Neural network approach for counting pedestrians

Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/8508

Title:	A kemelized Probabilistic Neural network approach for counting pedestrians
Authors:	Lim, Eng Aik Zarita, Zainuddin
Keywords:	Classification Counting system Data sets Image processing - methods Robabilistic neural networks Region of interest ISIE 2009 International Symposium on Industrial Electronics 2009
Issue Date:	5-Jul-2009
Publisher:	Institute of Electrical and Electronics Engineering (IEEE)
Citation:	p.2065-2068
Series/Report no.:	Proceedings of the International Symposium on Industrial Electronics (ISIE) 2009
Abstract:	An improved, intelligent pedestrian counting system, using images obtained from a single video camera, is described in this paper. This system is capable of detecting and counting a group of pedestrians in the region of interest. Groups can be extracted by using the image processing method, and a Kernel-induced Probabilistic Neural Network (KPNN) employed to perform the classification, and estimate the number of pedestrians in a group. We validated the pedestrian-counting system on a pedestrian dataset, and this analysis indicates th at the proposed KPNN-type classifier provides good results.
Description:	Link to publisher's homepage at http://ieeexplore.ieee.org/
URI:	http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5218897&tag=1 http://dspace.unimap.edu.my/123456789/8508
ISBN:	978-1-4244-4349-9
Appears in Collections:	Conference Papers

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