Autonomous Vehicular Surveillance using License Plate Recognition over Cloud Computing Architecture
Baby. D. Dayana1, Bikramjeet Chanda2, Vaidya Kabir Vinayak3, Sheikh Fahim Raza4
1Baby D Dayana, Assistant Professor, Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli, (Tamil Nadu), India.
2Bikramjeet Chanda, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, (Tamil Nadu), India.
3Vaidya Kabir Vinayak, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, (Tamil Nadu), India.
4Sheikh Fahim Raza, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, (Tamil Nadu), India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 4149-4153 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1389109119/2019©BEIESP | DOI: 10.35940/ijeat.A1389.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: License Plate Recognition (LPR) is the extraction and identification of licence plate numbers from license plates. The extraction process requires ample image pre-processing using normalization, gray scaling and edge removal techniques. These extracted plates can then be identified using image processing techniques such as neural networks and support vector machines. These license plates are captured using stationary video cameras, which extracts images from their feed as inputs into the image extraction algorithm. For the purposes of vehicular surveillance, these cameras are inefficient, as a lot of them will be required to monitor vehicles effectively. Hence there is a need for a larger scale model to carry out effective vehicular surveillance. For this purpose, the cameras embedded in self driving cars are utilised as replacements to stationary video cameras. These cameras have to advantage of being constantly mobile, hence being able to carry out a larger scale of surveillance. These cameras capture meaningful images of license plates from their video feed, and upload these images to the cloud using a Vehicular Cloud Computing (VCC) architecture. This centralized cloud carries out the image extraction and image processing tasks. The identified license plates can be used to monitor the cars they belong to. The cloud compares them to a database of license plates that are flagged by law enforcement. If the license plate is found to be flagged, then the respective law enforcement authorities are notified of the location of the car. If the plate belongs to a car with a history of misbehavior, the car capturing the plate is informed of thus, making it easier to safely navigate around the problematic driver.
Keywords: License Plate Recognition, Self Driving Cars, Vehicular Cloud Computing, Support Vector Machine.