Pedestrian Detection Project

This project demonstrates real-time pedestrian detection using the YOLOv4-tiny object detection model with OpenCV. It processes video frames, detects pedestrians, and annotates them with bounding boxes.

Prerequisites

Before running the script, ensure you have the following installed:

• Python 3.x
• OpenCV (pip install opencv-python)
• NumPy (pip install numpy)
• imutils (pip install imutils)

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/pedestrian-detection.git
   cd pedestrian-detection

2.Download YOLOv4-tiny model files:

• Download yolov4-tiny.weights and yolov4-tiny.cfg from the official YOLO website or other sources.
• Save these files into the project directory.

3.Obtain the COCO labels file (coco.names):

• The coco.names file contains a list of class names recognized by the YOLO model, including "person".
• Place coco.names in the project directory.

Usage

1.Run the pedestrian detection script:

python pedestrian_detection.py

2. Script Behaviour

• The script opens a video file and starts processing frames for pedestrian detection.
• Detected pedestrians are annotated with bounding boxes.
• Press the Esc key to exit the application.

Customization

• Input Video: Modify the video_path variable in the script (pedestrian_detection.py) to use a different video file.
• Detection Parameters: Adjust NMS_THRESHOLD and MIN_CONFIDENCE constants in the script for controlling detection sensitivity.
• Model Configuration: Customize the YOLO model by changing the configPath and weightsPath variables in the script.

Acknowledgments

• This project uses YOLOv4-tiny, a deep learning model developed by the Darknet team. Visit the official YOLO website for more information.
• The COCO dataset and labels (coco.names) are used for object recognition. Refer to the COCO website for dataset details.