TSVDS
TRAFFIC SIGNAL VIOLATION DETECTION SYSTEM
INTRODUCTION:
The Traffic Signal Violation Detection System is a computer vision-based project aimed at automatically detecting traffic signal violations at intersections. The system utilizes state-of-the-art object detection and image processing techniques to monitor vehicles' behavior and identify potential violations such as red light running and improper lane usage.
FEATURES:
1.Real-time object detection: The system employs deep learning models to detect and track vehicles, pedestrians, and other objects on the road in real-time.
2.Traffic signal violation detection: Using image processing techniques, the system identifies violations, such as vehicles running red lights or making illegal turns.
3.Video recording: The system records videos of detected violations, providing evidence for further analysis and potential law enforcement actions.
4.User-friendly interface: The project comes with an intuitive web-based interface that allows users to visualize the detection results and configure system parameters easily.
HOW IT WORKS:
1.Object Detection: The system uses a pre-trained deep learning model (e.g., YOLO, SSD, or Faster R-CNN) to detect vehicles and pedestrians in the video feed.
2.Violation Detection: By analyzing the object positions and traffic signal status, the system determines if a vehicle has violated any traffic rules, such as running a red light or making an illegal turn.
3.Alerts and Video Recording: When a violation is detected, the system triggers an alert and records the video segment of the violation, including the license plate for identification.
4.User Interface: Users can access the system through a web-based interface, where they can view live video streams, review recorded violations, and adjust system settings.
ACKNOWLEDGEMENTS:
We would like to acknowledge the following resources and projects that inspired or contributed to the development of this system:
OpenCV
TensorFlow Object Detection API
YOLO: Real-Time Object Detection