Nowadays in many of the developing countries, traffic rule violation has become a critical issue. Due to the increase in population, the use of vehicles is also increasing exponentially, leading to an increase in road accidents. Major reasons for these accidents are traffic rule violations, such as over speed and wrong direction driving. To monitor these violations, an enhanced real-time system is needed which has the ability of traffic rule violation detections from nearby CCTV cameras. The proposed traffic signal violation detection system performs tasks like speed violation detection and wrong-way direction detection, without human intervention. The proposed system mainly performs three tasks: vehicle detection, vehicle tracking and violation detection. The YOLOv3 deep learning method is used for locating vehicles moving on road, and then the Kalman filter is used for tracking of detected vehicles. After tracking each vehicle is assigned with an unique id, then speed violation detection algorithm and wrong-way direction detection algorithm vehicles are used to identify the vehicles which violated the traffic rules. The system is very efficient and accurate to identify vehicles moving in the wrong direction and over speed.
An Enhanced Real-Time System for Wrong-Way and Over Speed Violation Detection Using Deep Learning
Lect. Notes in Networks, Syst.
International Conference on Image Processing and Capsule Networks ; 2023 ; Bangkok, Thailand August 10, 2023 - August 11, 2023
Fourth International Conference on Image Processing and Capsule Networks ; Chapter : 21 ; 309-322
2023-11-18
14 pages
Article/Chapter (Book)
Electronic Resource
English
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