E3S Web Conf.
Volume 309, 20213rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)
|Number of page(s)||7|
|Published online||07 October 2021|
Detecting anomalous road traffic conditions using VGG19 CNN Model
1 MTech Student, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
2 Professor, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
Detection on the real time road traffic has tremendous application possibilities in metropolitan road safety and traffic management. Due to the effect of numerous factors, for example: climate, viewpoints and road conditions in real-time traffic scene, Anomaly detection actually faces many difficulties. There are many reasons for vehicle accidents, for example: crashes, vehicle on flames and vehicle breakdowns, which exhibits distinctive and obscure behaviours. In this paper, we approached with a model to identify oddity in street traffic by monitoring the vehicle movement designs in two unmistakable yet associated modes which is 1. The vehicle’s dynamic mode and 2. The vehicle’s Static mode. The vehicle’s static mode investigation is gained using the background modelling after the detection of a vehicle, this strategy is useful to locate the unusual vehicle movement which keep still out and about. The dynamic mode vehicle examination is gained from identified and followed vehicle directions to locate the strange direction which is distorted from the predominant movement designs. The outcomes from the double mode investigations are at long last fused together by driven a distinguishing proof model to get the last peculiarity. For this research we are using traffic-net Dataset, VGG19 CNN model along with ImageNet weights and OpenCV.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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