Mobile Basketball Shot Analytics Using Single-Camera Vision
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing real-time sports analytic systems for games like basketball are complex, expensive, and require multiple high-definition cameras and server-grade hardware, making them unsuitable for mass adoption, and there is a need for accurate detection of key events, player tracking, and minimal user input on a single mobile device.
Innovation Solution
A method and system using a single mobile device with a camera and convolutional neural networks (CNN) for real-time detection and analysis of basketball shots, tracking balls and players, generating shot analytics by identifying ball and shooter flows, and determining shot events, all while supporting self-calibration and minimal delay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple high-definition cameras and server-grade hardware are used for real-time sports analysis, then measurement precision and reliability are improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent combines multiple detection functions (ball detection, player detection, shot event recognition, trajectory tracking) into a single mobile device running a unified deep learning model. This integration eliminates the need for separate camera arrays and server infrastructure, achieving accurate real-time analysis while dramatically reducing system complexity.
Solution Approach 2:
The patent uses a single mobile device camera to capture and analyze sports events, replacing the need for multiple high-definition cameras. The deep learning model processes video frames from this single camera to extract all necessary analytical data, effectively using a simple copy (single camera) to achieve the functionality of a complex multi-camera system.
2Measurement precision
If multiple high-definition cameras and massive processing power are deployed, then detection accuracy and analysis capability are improved, but loss of energy and computational resources increase
Solution Approach 1:
The patent implements dynamic processing by adjusting the depth learning model's computational intensity based on the analysis stage. During real-time video processing, the model processes only key frames containing potential shot events rather than every frame, and uses lighter processing for tracking once objects are identified. This dynamic approach maintains high detection accuracy while significantly reducing overall computational resource consumption.
Solution Approach 2:
The patent applies partial action by focusing computational resources only on detecting and analyzing shot events rather than processing all player movements and game activities equally. The system identifies key moments (shot attempts) and concentrates processing power on these critical events, achieving accurate shot analysis with minimal overall computational expenditure.
3Measurement precision
If complex calibration and setup procedures are implemented, then measurement precision is improved, but ease of operation and adaptability deteriorate
Solution Approach 1:
The patent implements self-calibration through the deep learning model, which automatically adapts to different court environments, camera angles, and lighting conditions without requiring manual calibration procedures. The model learns to recognize court boundaries, basket positions, and player movements autonomously from the video input, eliminating complex setup procedures while maintaining high analysis accuracy across diverse environments.
Solution Approach 2:
The patent creates a universal analysis system that can handle various basketball courts, camera positions, and game scenarios through a single deep learning model. The model is designed to recognize and adapt to different environmental conditions automatically, providing consistent accurate analysis without requiring environment-specific calibration or configuration, thus achieving both precision and ease of operation.
Data Source
AI summary
Methods and systems for real-time generation of ball shot analytics are disclosed. The methods and systems perform steps for ball and posture detection, ball and posture flow generation, shot event identification and classification, and shot analytics generation based on identified shot events and shooter posture flow. Embodiments of the present invention use computer vision techniques to enable a resource-limited mobile device such as a smartphone to conduct the aforementioned steps. Therefore, the present invention may be implemented using a processor on a single mobile computing device. Also disclosed are benefits of the new methods, and alternative embodiments of implementation.