Sourav Paul
S.M Rafi Alam
Md. Orun Sunny
ABSTRACT
Bangladesh is rapidly getting urbanized. Recently traffic jams are caused mainly because of the growing number of motorcycles and cars and it implies the violation of traffic in Dhaka City. It causes severe destruction of property and more accidents that may endanger the living of the people. Due to recent incidents, it is very risky for pedestrians and drivers to consider full safety on the road. In this research, we present the construction of a system that makes the traffic sector easier than for the traffic police to operate the system, monitor traffic violations, and take action against the violations of traffic rules. Usually, traffic rules and signal violations are monitored via traffic camera by traffic police, or sometimes it is monitored manually. However, if motor vehicle users engage in any unethical behavior, the traffic police will have a difficult time detecting it. As a result, it would be highly beneficial if machine intelligence could assist traffic police in maintaining Dhaka City's traffic integrity. The system can classify traffic violations at a street intersection and investigate effective engagement strategies to identify traffic rule violations. We are using the transfer learning technique which is a surveillance system that uses two types of pre-trained CNN keras application models which names VGG16 and ResNet50 on images to read and detect the vehicles, helmet identification, no helmet identification, and bike triple riding. Our dataset is available online and presented in the paper. The Kaggle repository name is “traffic-violation-dataset-v3”. [21]
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Index Terms
- Image Analysis using Deep Learning for a Smart Traffic Control System
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