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Traffic Signal Violation Detection System

A Python desktop application that analyzes road video with YOLOv3-based vehicle detection and a Tkinter GUI, marking vehicles that cross a designated traffic line while it represents a red signal.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Traffic Signal Violation Detection System — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it to study or prototype video-based traffic-signal violation detection when the traffic boundary can be represented by a line drawn in the footage preview.

Who it is for

  • Developers learning to build a computer-vision application from scratch.
  • People exploring a GUI-based prototype for reviewing traffic video and highlighting suspected signal violations.

Documented uses

  • Open stored road footage in a desktop interface.
  • Detect and classify vehicles in video frames.
  • Mark a traffic line in the preview and flag vehicles crossing it when designated as red.
  • Review detected vehicles and suspected violations through bounding-box overlays.
Before you choose

Requirements & limits

The public repository lists a GPL-3.0 license.

  • The violation rule relies on a traffic line drawn over the road in the video preview and treated as the red-light boundary.
  • The repository identifies YOLOv3, Darknet-53, TensorFlow, OpenCV, and Tkinter in the detection and interface stack.
  • Quick start requires adjusting paths in two Python files and installing project dependencies; YOLOv3 weights may need to be obtained separately.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the project and open its desktop interface

Prepare the repository and launch the Tkinter application.

Before you start

  • Python environment with the project’s required dependencies installed
  • YOLOv3 weights file accessible to the project

Steps

  1. Clone the repository from GitHub.

  2. Change the directories in Project-GUI.py and object_detection.py.

  3. If yolov3.weights is not accessible, download the weights file linked by the project.

  4. Install the required Python dependencies in a virtual environment.

  5. Run python Project-GUI.py.

Expected outcome

The project’s GUI starts, ready for its documented video-opening workflow.

Guide limitations

  • The instructions do not enumerate the Python dependency versions.
  • The guide covers startup; it does not verify detection accuracy or operational suitability.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
16 October 2026
Original website title
anmspro/Traffic-Signal-Violation-Detection-System · Official homepage title · retained in its source language
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