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Code and Resource Attribution Declaration

This document serves as a declaration of all external code, libraries, APIs, or any resources used in the development of the project. It is mandatory to provide proper attribution for any components not authored by the submitting students.

Project Details:

Name: Jainil Rana (300362571)
Name: Omkar Harkare (300359531)

Project Name: Multi-View Foul Detection in Soccer

Description:
This project is an educational initiative utilizing the publicly available SoccerNet dataset to explore Multi-View Foul (MVFoul) detection in soccer matches. The primary objective is to develop and analyze deep learning models for automatic foul detection using data from multiple camera perspectives. This work is intended solely for educational and research purposes, without any commercial application.

Attribution Details:

  1. Code/API/Library Name: SoccerNet Dataset

    • Source: [https://soccer-net.org/]
    • Usage: Used as the primary dataset for training and evaluating the foul detection models.

  2. Code/API/Library Name: PyTorch

    • Source: [https://pytorch.org/]
    • Usage: Utilized as the main deep learning framework for implementing ResNetR3D and TwoStreamModel architectures.

  3. Code/API/Library Name: ResNetR3D Architecture

    • Source: Referenced implementation details from publicly available research papers and repositories.
    • Usage: Adapted for video classification tasks in foul detection.

  4. Code/API/Library Name: TwoStreamModel

    • Source: Referenced concepts from related works on two-stream approaches in video analysis.
    • Usage: Investigated its application for multi-view foul detection.

  5. Code/API/Library Name: sn-mvfoul Project (Reference Only)

    • Source: [https://github.com/SoccerNet/sn-mvfoul]
    • Usage: Used purely for reference to understand problem domain and evaluation metrics. No code or direct implementation was used or modified.

  6. Code/API/Library Name: SoccerNet-MLV Project (Reference Only)

  7. Code/API/Library Name: FastAPI

    • Source: [https://fastapi.tiangolo.com]
    • Usage: Used as the primary framework for building the web API. FastAPI was utilized for its high performance, automatic data validation, and interactive API documentation features.

  8. Code/API/Library Name: Uvicorn

    • Source: [https://www.uvicorn.org/]
    • Usage: Used as the ASGI server to run the FastAPI application in development and testing environments.

  9. Code/API/Library Name: Pydantic

  10. Code/API/Library Name: Starlette

    • Source: [https://www.starlette.io/]
    • Usage: Underlying web framework utilized by FastAPI for routing, middleware, and asynchronous request handling.

  11. Code/API/Library Name: Python Standard Libraries

    • Source: [https://docs.python.org/3/library/]
    • Usage: Utilized various standard libraries such as typing for type hints and asyncio for asynchronous programming in Python.

Academic Integrity Notice:

I, Jainil Rana (300362571), solemnly declare that all external code, libraries, APIs, or any resources used in this project have been properly attributed in this Declaration.txt file. I understand that failure to declare external resources or providing misleading information may be considered academic fraud and can result in severe consequences.

Jainil Rana
21-11-2024

I, Omkar Harkare (300359531), solemnly declare that all external code, libraries, APIs, or any resources used in this project have been properly attributed in this Declaration.txt file. I understand that failure to declare external resources or providing misleading information may be considered academic fraud and can result in severe consequences.

Omkar Harkare
21-11-2024

Educational Purpose:

This project is solely for educational and research purposes. It is not intended for commercial use or deployment in real-world scenarios.

Disclaimer:

This project is a learning exercise and should not be used for any official or commercial purposes related to sports officiating or decision-making.