|
UPSI Digital Repository (UDRep)
|
|
| ||||||||||||||||||||||
| Abstract : Universiti Pendidikan Sultan Idris |
| Due to the increasing number of vehicles accessing higher education campus nowadays, it is difficult to check the rule regulations, enforcement and security. Currently, the security checkpoint is still using the manual procedures to track vehicle driver’s entrance. At the same time, it is timeconsuming for an officer to check the car sticker/plate every day physically. Also, it is not workable for a hire full-time security officer just to check on the sticker/car plate. As a solution, an artificial intelligence system to identify the car plate has been building up. By implementing this intelligence system, it’s capable of enhancing the management, security and controlling of vehicles accessing by using license plate recognition. If a vehicle approaches the entry gate, the recognition system will initialize and execute license plate numbers to the database system. During the exits through the departure gate, the same method repeated. The archives of vehicle entries and departures imposed for parking fee calculations and marketing statistics. Additionally, the list can be compared at any time with actual parked cars for security reasons.
|
| References |
1. Kocer, H. E., & Cevik, K. K. (2011). Artificial neural networks based vehicle license plate recognition. Procedia Computer Science, 3, 1033-1037. doi:10.1016/j.procs.2010.12.169 2. Roy, A., & Ghoshal, D. P. (2011). Number Plate Recognition for use in different countries using an improved segmentation. 2011 2nd National Conference on Emerging Trends and Applications in Computer Science. doi:10.1109/ncetacs.2011.5751407 3. Öztürk, F., & Özen, F. (2012). A New License Plate Recognition System Based on Probabilistic Neural Networks. Procedia Technology, 1, 124-128. doi:10.1016/j.protcy.2012.02.024 4. Roy, A. (2008). A Study of Car Park Control System Using Optical Character Recognition. 2008 International Conference on Computer and Electrical Engineering. doi:10.1109/iccee.2008.81 5. Vishwanath, N., Somasundaram, S., Ariharan, M., & Nallaperumal, K. (2012). A novel edge based detection and PANDA framework approach for Indian license plate character recognition. 2012 IEEE International Conference on Computational Intelligence and Computing Research. doi:10.1109/iccic.2012.6510320 6. Liang, Z., & Zhang, S. (2013). A Location Method for Multi-style License Plates Based on Character Classification. Journal of Convergence Information Technology, 8(4), 226-234. doi:10.4156/jcit.vol8.issue4.27 7. Zhang, Z., & Wang, C. (2012). The Research of Vehicle Plate Recognition Technical Based on BP Neural Network. AASRI Procedia, 1, 74-81. doi:10.1016/j.aasri.2012.06.013 8. Anagnostopoulos, C., Anagnostopoulos, I., Loumos, V., & Kayafas, E. (2006). A License PlateRecognition Algorithm for Intelligent Transportation System Applications. IEEE Transactions on Intelligent Transportation Systems, 7(3), 377-392. doi:10.1109/tits.2006.880641 9. Yang, Y., Gao, X., & Yang, G. (2011). Study the Method of Vehicle License Locating Based on Color Segmentation. Procedia Engineering, 15, 1324-1329. doi:10.1016/j.proeng.2011.08.245 10. Rafique, S., Iqbal, M., & Habib, H. A. (2009). Space invariant vehicle recognition for toll plaza monitoring and auditing system. 2009 IEEE 13th International Multitopic Conference. doi:10.1109/inmic.2009.5383107 11. Wu, H., & Li, B. (2011). License plate recognition system. 2011 International Conference on Multimedia Technology. doi:10.1109/icmt.2011.6003138 12. Meng, B., & Han, G. (2013). Electronic image stabilization algorithm using improved scale invariant feature transform. Journal of Computer Applications,32(10), 2817-2820. doi:10.3724/sp.j.1087.2012.02817 |
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |