mobile based application

Traffic violation detection System

Architechture

One of the top priorities in smart cities is implementing an efficient traffic management system. Such systems are essential for regulating traffic flow, preventing accidents, and reducing congestion. However, several challenges persist due to the increasing number of vehicles, a lack of sufficient traffic management personnel, and occasional failures to detect traffic violations. Speeding drivers and those who violate traffic rules through unsafe lane changes or maneuvers significantly contribute to the rising number of collisions. This critical issue demands immediate attention to prevent unnecessary loss of lives.

Current traffic control systems, which are largely managed by human operators, are insufficient to handle the complexities of excessive congestion effectively. To address this, we propose a hybrid model that leverages machine learning and advanced object detection techniques. The model integrates TensorFlow, a machine learning platform, with the "You Only Look Once" (YOLO) object detection framework, specifically an enhanced version of the YOLOv3 algorithm for vehicle detection. Python is used as the programming language for this implementation.

The proposed system gathers data from surveillance cameras installed near traffic signals, which are connected to the city's traffic servers. When a vehicle violates traffic rules, the system detects the infraction in real-time and promptly transmits this information to the server. By automating traffic monitoring and violation detection, this solution aims to enhance traffic management efficiency and improve road safety in smart cities.

Hardware requirement