Effortlessly count cars in videos using YOLOv8-powered computer vision program for streamlined traffic monitoring.

This Python script uses computer vision techniques to count vehicles in a traffic cam video. The implementation is based on the YOLO (You Only Look Once) object detection model, integrated with a SORT (Simple Online and Realtime Tracking) algorithm for tracking.

• Object Detection: Utilizes the YOLO model to detect vehicles in the video.

• Tracking: Implements the SORT algorithm for real-time tracking of detected vehicles.

• Counting: Tracks and counts vehicles crossing predefined lines in the video.

• Visualization: Displays the output with graphical overlays indicating the total count and count in specific directions.

Make sure you have the following dependencies installed:

• Ultralytics YOLO

• OpenCV (cv2)

• cvzone

• SORT (Simple Online and Realtime Tracking)

1. Clone the repository:

   git clone https://github.com/serialdotai/car-counter.git
   cd car-counter

2. Install the required packages:

   pip install -r requirements.txt

3. Download the YOLO weights file yolov8l.pt and place it in a yolo weights directory.

4. Run the script:

   python main.py

Adjust the following parameters in the script as needed:

• lineUp and lineDown: Define the lines for counting vehicles in the video.
• mask: Set the mask image for region of interest.

The processed video will be saved as result.mp4 in the same directory.

• Ultralytics YOLO
• SORT