Store Deadspot Detection

Overview

This project is a Streamlit application that analyzes store video footage to detect customer movement patterns and identify "deadspots" - areas with low customer traffic. It uses computer vision techniques to detect people in the video and generates a heatmap of customer footfall. The app also provides a detailed report of items located in deadspot areas.

Features

• Real-time people detection in store video footage
• Generation of customer footfall heatmap
• Identification of store deadspots
• Detailed report of products in deadspot areas, including category, brand, and weekly sales

Requirements

• Python 3.7+
• Streamlit
• OpenCV
• NumPy
• Pandas
• Matplotlib
• Seaborn

Installation

1. Clone this repository: https://github.com/shinjini1202/StoreDeadspots.git
2. Install the required packages: pip install -r requirements.txt

Usage

1. Ensure you have the following files in the project directory:

• store.mp4: Video file of the store
• deploy.prototxt: Network architecture file for the detection model
• mobilenet_iter_73000.caffemodel: Pre-trained weights for the detection model
• store.csv: CSV file containing store product data

2. Run the Streamlit app: streamlit run app.py

3. The app will open in your default web browser. You will see:

• The store video feed with bounding boxes around detected people
• A heatmap showing customer footfall
• A table displaying deadspot locations and associated product information