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dc.contributor.authorLim, Eng Aik-
dc.contributor.authorZarita, Zainuddin-
dc.date.accessioned2010-08-06T09:36:12Z-
dc.date.available2010-08-06T09:36:12Z-
dc.date.issued2009-07-05-
dc.identifier.citationp.2065-2068en_US
dc.identifier.isbn978-1-4244-4349-9-
dc.identifier.urihttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5218897&tag=1-
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/8508-
dc.descriptionLink to publisher's homepage at http://ieeexplore.ieee.org/en_US
dc.description.abstractAn 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.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineering (IEEE)en_US
dc.relation.ispartofseriesProceedings of the International Symposium on Industrial Electronics (ISIE) 2009en_US
dc.subjectClassificationen_US
dc.subjectCounting systemen_US
dc.subjectData setsen_US
dc.subjectImage processing - methodsen_US
dc.subjectRobabilistic neural networksen_US
dc.subjectRegion of interesten_US
dc.subjectISIE 2009en_US
dc.subjectInternational Symposium on Industrial Electronics 2009en_US
dc.titleA kemelized Probabilistic Neural network approach for counting pedestriansen_US
dc.typeWorking Paperen_US
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