| dc.contributor.author | Lim, Eng Aik | |
| dc.contributor.author | Zarita, Zainuddin | |
| dc.date.accessioned | 2010-08-06T09:36:12Z | |
| dc.date.available | 2010-08-06T09:36:12Z | |
| dc.date.issued | 2009-07-05 | |
| dc.identifier.citation | p.2065-2068 | en_US |
| dc.identifier.isbn | 978-1-4244-4349-9 | |
| dc.identifier.uri | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5218897&tag=1 | |
| dc.identifier.uri | http://dspace.unimap.edu.my/123456789/8508 | |
| dc.description | Link to publisher's homepage at http://ieeexplore.ieee.org/ | en_US |
| dc.description.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. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineering (IEEE) | en_US |
| dc.relation.ispartofseries | Proceedings of the International Symposium on Industrial Electronics (ISIE) 2009 | en_US |
| dc.subject | Classification | en_US |
| dc.subject | Counting system | en_US |
| dc.subject | Data sets | en_US |
| dc.subject | Image processing - methods | en_US |
| dc.subject | Robabilistic neural networks | en_US |
| dc.subject | Region of interest | en_US |
| dc.subject | ISIE 2009 | en_US |
| dc.subject | International Symposium on Industrial Electronics 2009 | en_US |
| dc.title | A kemelized Probabilistic Neural network approach for counting pedestrians | en_US |
| dc.type | Working Paper | en_US |
