In-Store Analytics using Object Detection

This project implements Object Detection for performing In-Store Analytics using yolov8n as the per-trained model. This can help in getting using insights for the store manager to analyze and boost up yhe sales by monitoring the crowd at a particular instance in the store. To learn more about the project please refer this article.

Table of Contents

• Introduction
• Features
• Installation
• Execution
• Contact

Introduction

In this project, we used YOLOv8 model by Ultralytics for efficiently detecting customers from the video frames in real-time. This can help in getting useful data for the store manager to increase sales.

Features

• Fast and efficient method
• Uses yolov8n model
• Real time support
• Optimizable to increase accuarcy

Installation

1. Clone the repository:

   git clone https://github.com/vansh-khaneja/In-Store-Analytics-using-Object-Detection
   cd In-Store-Analytics-using-Object-Detection

2. Set up the Python environment and install dependencies:

   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

Execution

1.Download the sample Video Dataset for this project here or you can try with your own dataset. Just change the path of the Video here.

    input_video_path = "c:/Users/testing.mp4"

2.You can also change the model based on the use case. Here in this project we have used yolov8n.pt model. Please refer Ultraytics documentation for learning about different model sizes. You can change the model here.

    from ultralytics import YOLO
    model = YOLO("yolov8n.pt")

3.Execute the main.py file by running this command in terminal.

    python main.py

Contact

For any questions or issues, feel free to open an issue on this repository or contact me at [email protected].

Happy coding!