This is a Python-based project that uses the YOLOv8 object detection model and the SORT (Simple Online and Realtime Tracking) algorithm to count vehicles in a video feed. 🎥
- Real-time vehicle detection and counting 🔍
- Supports various vehicle types (cars, trucks, buses, motorbikes) 🚗🚌🏍️
- Utilizes a custom road mask to focus the detection on the desired region 🛣️
- Tracks individual vehicles across frames using the SORT algorithm 🔍🤖
- Python 3.x
- OpenCV 🖥️
- Ultralytics YOLO 🤖
- SORT (Simple Online and Realtime Tracking) 🔍
- cvzone 🎨
- The code loads the YOLOv8 model and initializes the SORT tracker. 🔍
- It then processes each frame of the video, detecting vehicles using the YOLOv8 model and vehicle types (cars, trucks, buses, bikes). 🚘
- The detected vehicles are then passed to the SORT tracker, which keeps track of individual vehicles across frames. 🔍
- The script draws bounding boxes around the detected vehicles, displays the vehicle IDs, and counts the number of unique vehicles that have passed through the linr🔢
Follow these steps to run project :
-
Clone the repository:
git clone https://github.com/hetbhalani/Car_Counter.git cd Car_Counter -
Install dependencies :
pip install requirements.txt
-
Run the app:
python CarCounter.py
If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request. 🙌
Special thanks to Alex Bewley, the creator of the SORT (Simple Online and Realtime Tracking) algorithm, for his valuable contribution to the field of object tracking.