1.Abstract

Road accidents have become a major global concern, with overspeeding identified as one of the primary causes of severe collisions and fatalities. Traditional speed monitoring methods such as manual surveillance and radar-based systems are often limited in coverage, costly to deploy at scale, and unable to provide continuous real-time monitoring across all road sections. To address these limitations, this project proposes an AI-based Vehicle Speed Tracking System using computer vision and deep learning techniques.

The proposed system utilizes video input from CCTV cameras installed on roads and highways to monitor moving vehicles in real time. The system is built using the YOLOv8 (You Only Look Once version 8) object detection model, which is capable of accurately detecting and identifying vehicles in each video frame. Once the vehicles are detected, computer vision techniques are applied to track their movement across consecutive frames.

By calculating the displacement of detected vehicles over time, the system estimates the real-time speed of each vehicle. This computed speed is then compared with predefined speed limits to determine whether the vehicle is moving within permissible limits or violating traffic regulations. If overspeeding is detected, the system automatically generates an alert.

The alert mechanism can be integrated with a notification system or mobile application, enabling traffic authorities or monitoring systems to receive instant warnings about overspeeding vehicles. This allows for faster response and improved traffic enforcement.

The main objective of this project is to enhance road safety by providing an automated, accurate, and real-time speed monitoring solution. By combining YOLOv8-based object detection with computer vision tracking techniques, the system reduces dependency on manual monitoring and improves efficiency in identifying traffic violations.

In conclusion, the Vehicle Speed Tracking System demonstrates the effective use of artificial intelligence and computer vision in smart traffic management. It offers a scalable and cost-effective solution for detecting overspeeding vehicles, thereby contributing to the reduction of road accidents and improving overall transportation safety.

2. Objectives

1. To detect and track vehicles in real time.
2. To calculate vehicle speed using video analysis.
3. To identify over-speeding vehicles.
4. To improve traffic monitoring and law enforcement.
5. To automate speed detection without manual intervention.

3.Existing System

In the existing traffic monitoring systems, vehicle speed detection is mainly carried out using traditional methods such as radar guns, speed cameras, and manual surveillance by traffic authorities. These systems are widely used for detecting overspeeding vehicles and enforcing traffic rules on roads and highways. Radar-based systems measure the speed of a vehicle using radio waves, while speed cameras capture images of vehicles violating speed limits. Manual monitoring is also used in some cases where traffic police physically observe and control vehicle speed.

However, these existing systems have several limitations. Radar-based systems are expensive to install and maintain, and they cover only limited road sections. Manual monitoring requires continuous human effort and is prone to errors and inefficiency. Image-based speed detection systems also face challenges in real-time analysis, especially in complex traffic environments with heavy vehicle flow, occlusions, and varying lighting conditions.

Limitations:

1. Expensive hardware setup.
2. Limited coverage area.
3. Requires manual operation in some cases.
4. Difficult to scale across multiple locations.

4.Proposed System

The proposed system is an AI-based Vehicle Speed Tracking System that uses computer vision and deep learning techniques to automatically detect vehicles and estimate their speed in real time. The system utilizes CCTV camera footage installed on roads and highways to continuously monitor traffic flow without human intervention.

YOLOv8 (You Only Look Once version 8) is used for real-time vehicle detection in video frames. After detecting vehicles, computer vision tracking techniques are applied to track the movement of each vehicle across consecutive frames. Based on the displacement of vehicles over time, the system calculates the speed of each vehicle using frame rate and distance estimation methods.

Whenever a vehicle exceeds the predefined speed limit, the system identifies it as overspeeding and generates an alert. This alert can be sent to traffic authorities or displayed on a monitoring dashboard for immediate action. The system provides a fully automated and intelligent solution for traffic monitoring using existing CCTV infrastructure.

5. Implementation Procedure

Module 1: Environment Setup

1. Install Python and required libraries.

Module 2: Video Input

1. Capture video from CCTV or webcam.
2. Preprocess frames for detection.

Module 3: Vehicle Detection

1. Use YOLO model to detect vehicles.
2. Draw bounding boxes around detected vehicles.

Module 4: Object Tracking

1. Assign unique IDs to vehicles.
2. Track movement across frames.

Module 5: Speed Calculation

1. Define distance between two reference lines.
2. Measure time taken for a vehicle to cross the lines.
3. Calculate speed using distance/time formula.

Module 6: Alert System

1. Identify over-speeding vehicles.
2. Trigger alerts or log violations.

Module 7: Visualization

1. Display speed values on screen.
2. Show bounding boxes and tracking IDs.

6.Software Requirements

Operating System : Windows 10 / Linux

Programming Language : Python

Libraries : OpenCV, NumPy, YOLOv8

Framework : PyTorch / TensorFlow

IDE : VS Code / Jupyter Notebook

7.Hardware Requirements

Processor : Intel i3 or above

RAM : 4 GB (8 GB recommended)

Hard Disk : 500 GB

Camera : CCTV / Video input source

GPU : Optional (for faster processing)

8. Advantages of the Project

1. Real-time speed monitoring.
2. Reduces dependency on expensive hardware.
3. Can be integrated with existing surveillance systems.
4. Improves traffic law enforcement.
5. Scalable for smart city applications.
6. Automated and efficient system.