Smart Court System for Real-Time Sports Analysis
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Solution Overview
Problem
Current video analysis systems for sports require extensive preparation, manual tagging of events, and human intervention for calibration and analysis, making them inefficient for real-time data processing and debriefing in constrained sport environments.
Innovation Solution
The Smart-court system employs automatic recording and calibration using multiple cameras, a data processing system with object detection and event analysis modules for real-time tracking and classification of sports activities, enabling instant debriefing and online publishing of game statistics and videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual recording, calibration procedure and uploading of footage are used, then video analysis can be performed, but preparation time is excessive
Solution Approach 1:
The system performs automatic calibration and setup procedures before the actual sport session begins. The cameras are pre-positioned and the system automatically calibrates itself using detection of court markings and objects, so that when the sport session starts, the system is already ready for real-time analysis without requiring manual preparation during the event.
Solution Approach 2:
The system performs self-calibration by automatically detecting court markings, boundaries, and other reference objects in the environment. The calibration process is executed autonomously by the processing unit which identifies geometric features and adjusts camera parameters automatically, eliminating the need for human operators to perform manual calibration procedures.
2Loss of information
If manual tagging of events is performed, then event classification can be achieved, but analysis efficiency is reduced
Solution Approach 1:
The system replaces the manual mechanical process of event tagging with an automated computer vision-based detection system. The processing unit continuously analyzes video streams from multiple cameras, automatically identifies sport-specific events based on object tracking and motion analysis, and classifies them without human intervention, thereby maintaining classification accuracy while dramatically improving analysis efficiency.
Solution Approach 2:
The system introduces an intermediate automated event detection layer between the raw video footage and the final analysis output. This intermediary processing stage automatically identifies and tags events by analyzing object trajectories, motions, and interactions, serving as a bridge that translates visual data into structured event information without requiring manual intervention.
3Area of stationary object
If a large array of cameras is used, then comprehensive coverage is achieved, but system complexity and setup requirements increase
Solution Approach 1:
The system uses a multi-functional camera setup where each camera is capable of capturing multiple types of data (video footage, object detection, motion tracking) and serving multiple purposes simultaneously. The same camera array used for recording can also perform automatic calibration, event detection, and analysis, reducing the need for separate specialized equipment and simplifying the overall system architecture.
Data Source
AI summary
A Smart-court system, adaptive to constrained sport environment, for enabling real time analysis and debriefing of sport activities is provided herein. The Smart-court system is comprised of: (i) an automatic recording system comprising a plurality of video cameras located in a court, arranged to real-time (RT) recording of a sport session and utilizing automatic calibration and stabilization module; and (ii) a data processing system comprising: (a) a capture module for grabbing a video stream; (b) an objects' detector module arranged to extract during the RT sport session, the objects from the foreground of each frame; (c) an event module for automatically analyzing, the motion and the activities of the tracked objects for automatically identifying and classifying events, creating a synchronized event log and calculating statistics that occurred during the RT sport session; and (d) a presentation module enabling to perform instant debriefing, combined biomechanical and tactical analysis of the video.