Detecting, Classifying, and Mapping Retail Storefronts

This repository contains the implementation of the paper "Detecting, classifying, and mapping retail storefronts using street-level imagery" by Shahin Sharifi Noorian, Sihang Qiu, Achilleas Psyllidis, Alessandro Bozzon, and Geert-Jan Houben. The paper was presented at the 2020 International Conference on Multimedia Retrieval.

The project introduces a novel method for automatically detecting, geo-locating, and classifying retail stores and related commercial functions, on the basis of storefronts extracted from street-level imagery. It presents a deep learning approach that takes storefronts from street-level imagery as input, and directly provides the geo-location and type of commercial function as output.

Repository Structure

The repository is structured as follows:

• crowdsourcing/: Contains the Angular project for crowdsourcing.
• input/: Directory for input data.
• labels/: Contains label files like categories.txt and google_label.txt.
• location_estimator/: Contains Python scripts for location estimation.
• main_video.py: Main script for video processing.
• main.py: Main script for image processing.
• model_weights: Directory for model weights.
• shop_detector/: Contains Python scripts for shop detection.
• shop_recognizer/: Contains Python scripts for shop recognition.

Getting Started

Prerequisites

• Python 3.6 or higher
• Angular CLI 8.3.21 or higher

Installation

1. Clone the repository.
2. Install Python dependencies: pip install -r requirements.txt.
3. Navigate to the crowdsourcing/ directory and install Angular dependencies: npm install.

Usage

Image Processing

Run python main.py to start the image processing.

Video Processing

Run python main_video.py to start the video processing.

Crowdsourcing

Navigate to the crowdsourcing/ directory.

1. Run ng serve for a dev server. Navigate to http://localhost:4200/. The app will automatically reload if you change any of the source files.
2. Run ng build to build the project. The build artifacts will be stored in the dist/ directory. Use the --prod flag for a production build.

Testing

Navigate to the crowdsourcing/ directory.

• Run ng test to execute the unit tests via Karma.
• Run ng e2e to execute the end-to-end tests via Protractor.

Citation

If you use this code in your research, please cite the paper:

@inproceedings{sharifi2020detecting,
  title={Detecting, classifying, and mapping retail storefronts using street-level imagery},
  author={Sharifi Noorian, Shahin and Qiu, Sihang and Psyllidis, Achilleas and Bozzon, Alessandro and Houben, Geert-Jan},
  booktitle={Proceedings of the 2020 international conference on multimedia retrieval},
  pages={495--501},
  year={2020}
}

License

This project is licensed under the MIT License - see the LICENSE.md file for details.