Car Detection and Car Counter

This project utilizes the YOLOv8l model for car detection and implements a Simple Online and real-time tracking (SORT) algorithm for counting the number of cars in a video stream. The system detects cars within a predefined region of interest and tracks them using unique IDs.

Requirements

• Python 3.x
• OpenCV
• NumPy
• Ultralytics YOLO
• SORT (Simple Online and Realtime Tracking)

Installation

1. Clone the repository:

git clone https://github.com/rajveersinghcse/Car-Detection-and-Car-Counter.git

2. Install the required packages:

pip install -r requirements.txt

Usage

1. Ensure you have a video file named cars.mp4 in the project directory.
2. Download the YOLOv8l model weights (yolov8l.pt) from the Ultralytics YOLO repository and place them in the project directory.
3. Run the following command to start the car detection and counting process:

python car_counter.py

Description

• car_counter.py: This script performs car detection and counting using YOLOv8l for object detection and SORT for object tracking. It reads frames from the cars.mp4 video, applies a mask to isolate the region of interest, detects cars within this region, tracks them using SORT, and counts the total number of unique cars.

Acknowledgments

• YOLOv8l: Ultralytics YOLO - GitHub Repository
• SORT: Simple Online and Realtime Tracking - GitHub Repository

License

This project is licensed under the MIT License.