YOLOv8 People Counting and Tracking

Overview

This project utilizes YOLOv8, a state-of-the-art real-time object detection system, to count people entering a bus and track their movements. The goal is to provide an efficient and accurate solution for monitoring passenger traffic.

Project Structure

1. Installation

Ensure you have the necessary dependencies installed by following the instructions in the requirements.txt file.

pip install -r requirements.txt

2. Data Preparation

Prepare your dataset or use pre-existing datasets containing bus footage for training and testing.

3. Model Training

Train the YOLOv8 model on your dataset to enable it to detect and track people entering the bus. Update configurations and hyperparameters as needed.

python train.py --data data.yaml --cfg yolov8-custom.cfg --weights '' --batch-size 16 --epochs 300 --device 0

4. Inference

Run the trained model on new bus footage to count and track people. Adjust confidence thresholds and other parameters for optimal results.

python detect.py --source <your_video_or_image_path> --weights <path_to_trained_weights> --conf 0.5

5. Results and Analysis

Evaluate the model's performance, analyze results, and fine-tune parameters if necessary. This may involve adjusting IOU thresholds, confidence levels, etc.

Important Notes

Ensure CUDA and cuDNN are properly configured for GPU acceleration.
Customize the YOLOv8 configuration file (yolov8-custom.cfg) based on your specific requirements.
Use the --weights flag in detect.py to load the trained weights for inference.

Acknowledgments

This project builds upon the YOLOv8 architecture, and we extend our gratitude to the YOLO community for their contributions.

habiba-abouelseoud/YOLOv8-People-Counting-and-Tracking