Video Recognition App for Football Game Analysis - Phase 3 & 4

Overview This specification outlines the requirements for a video recognition app designed to analyze football games by recognizing player movements and providing in-depth game analysis. The app is intended for use by football coaches, analysts, and enthusiasts to gain insights into player performance, team tactics, and overall game dynamics. Functional Requirements 1. Video Input The app should support the import of football game videos in various formats (e.g., MP4, AVI, MKV). Users should be able to capture video footage directly using the device's camera. 2. Player Detection and Tracking The app should detect and track players throughout the video. It should be able to distinguish between players from both teams and assign unique identifiers. Tracking should be accurate and robust, even in the presence of occlusions or players leaving and re-entering the frame. 3. Event Recognition The app should recognize various football events, such as passes, tackles, goals, fouls, and offside situations. It should timestamp and annotate these events in the video timeline. 4. Tactical Analysis Provide tools to create and analyze heatmaps showing player positioning and movement on the field. Support for drawing and annotating tactical diagrams on the video to explain strategies and tactics. 5. Statistics and Metrics Calculate and display key player and team statistics, such as distance covered, speed, and pass completion rates. Offer insights into player and team performance based on the collected data. 6. Video Analysis Allow users to slow down, speed up, or pause the video for a frame-by-frame analysis. Support side-by-side video comparison to analyze player movements and tactics in a split-screen view. 7. Data Export Enable users to export the analyzed data, annotated videos, and tactical diagrams for further use and sharing. 8. User Interface Design an intuitive and user-friendly interface that allows users to interact with the video and analysis tools efficiently. Support both desktop and mobile devices for flexibility in use. Non-functional Requirements 1. Performance The app should provide real-time or near-real-time analysis, even for longer football games. Ensure smooth playback and responsiveness during video analysis. 2. Accuracy Player detection and event recognition should be highly accurate, minimizing false positives and negatives. 3. Security Implement strong security measures to protect user-generated content and data, especially when exporting analysis results. 4. Compatibility Ensure the app works on a variety of devices and operating systems, including Windows, macOS, iOS, and Android. 5. Scalability The app should be able to handle large video files and long-duration football games without performance degradation. 6. Customization Provide options for users to customize the analysis parameters, such as event recognition rules and data presentation. Technical Stack The app should use a combination of computer vision, machine learning, and video processing techniques. Technologies like OpenCV, deep learning frameworks (e.g., TensorFlow or PyTorch), and video codecs should be considered. Testing Rigorous testing should be conducted to ensure the accuracy and robustness of player detection, event recognition, and analysis features. Maintenance and Support Regular updates and bug fixes to maintain the app's reliability and compatibility with new devices and formats. Documentation Provide comprehensive documentation for users and developers to understand how to use the app and integrate it with other systems. Conclusion This specification outlines the key features and requirements for a video recognition app focused on analyzing football games. By meeting these requirements, the app aims to provide valuable insights to football coaches, analysts, and enthusiasts for improved player and team performance analysis. First phase aims to make visual idenditication and tracing engine