IoT with Cloud Centric Vehicle Detection and Counting System for Smart Traffic Surveillance
S. Saravanan1, K. Venkatachalapathy2
1S. Saravanan*, Assistant Professor/Programmer Department of Computer and Information Science, Annamalai University, Chidambaram, India.
2Dr. K. Venkatachalapathy, Professor, Department of Computer and Information Science, Annamalai University, Chidambaram, India.
Manuscript received on November 02, 2019. | Revised Manuscript received on November 15, 2019. | Manuscript published on December 30, 2019. | PP: 4862-4866 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4255129219/2019©BEIESP | DOI: 10.35940/ijeat.B4255.129219
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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: The efficient management of road traffic is one primary facet of many, in smart cities. Traffic overcrowding can be managed successfully, if prior estimation of the number of vehicles that will pass though a crowded junction in a specific time is known. This paper introduces a methodology which targets vehicle extraction on videos covering vehicles. To resolve the problem of current vehicle detection such as the need of detection accuracy and slow speed, an improved YOLOv3 vehicle detection is utilized. The k-means clustering used to group the bounding box around the vehicle in training dataset. The method for calculation of loss with respect to the length and width of the bounding boxes was recovered through the implementation of the batch normalization process. Finally, to improve the feature extraction of the network the high repeated convolution layer are removed. The experiment results are carried out on the BIT-vehicle validation datasets which shows the improvement of mean Average Precision (mAP) could certainly reach 95.6%.
Keywords: Traffic Surveillance, YOLOv3, k-means Clustering, accuracy, IoT.