Automated Video Highlight Generation Using Jersey Recognition
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Solution Overview
Problem
Low-funded sports teams face challenges in creating highlight reels from video footage due to high costs and time-consuming editing processes, especially in identifying specific player actions and synchronizing video from multiple angles and devices, which limits their ability to improve performance analysis and fan engagement.
Innovation Solution
A system and method utilizing character recognition to identify players by their jersey numbers, track their movements, and synchronize video footage from multiple devices in real-time or post-processing, enabling automated editing of highlight reels and sharing of video content through a mobile app and server-based processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual video editing is used to identify specific player actions and synchronize video from multiple devices, then video quality and accuracy are improved, but time consumption and cost increase significantly
Solution Approach 1:
The system enables automated self-service video editing by having the computer vision system automatically identify players, track their movements, detect actions, and synchronize video clips without human intervention. The system processes video footage autonomously to generate highlight reels, eliminating the need for manual editing while maintaining high accuracy in player action identification.
Solution Approach 2:
The patent replaces the mechanical manual editing process with an automated computer vision system that uses image processing, machine learning, and algorithms to identify players by jersey numbers, track movements, detect actions, and synchronize video. This substitution of manual mechanical editing with automated digital processing dramatically reduces time consumption while preserving identification accuracy.
2Manufacturing precision
If professional video editors are hired to create highlight reels, then video production quality is improved, but cost increases
Solution Approach 1:
The system provides self-service video production capabilities to coaches, players, and teams, eliminating the need to hire professional video editors. The automated system performs all editing tasks including player identification, action detection, clip selection, and video synchronization, allowing users to produce professional-quality highlight reels independently at minimal cost.
Solution Approach 2:
The patent employs cost-effective mobile devices (smartphones, tablets) as video capture equipment instead of expensive professional cameras. These readily available devices are sufficient for capturing game footage, and the system processes this footage to produce high-quality outputs, significantly reducing equipment and production costs while maintaining acceptable video quality.
3Adaptability or versatility
If multiple video devices are used to capture game footage from different angles, then video content variety and analysis capability are improved, but synchronization complexity and processing difficulty increase
Solution Approach 1:
The system replaces complex manual synchronization processes with automated digital timestamp correlation. Each video device captures footage with embedded timestamps, and the computer vision system automatically correlates these timestamps across multiple devices to synchronize clips from different angles. This automated approach handles the complexity of multi-device synchronization without requiring manual coordination, enabling seamless integration of footage from numerous devices.
Solution Approach 2:
The patent introduces a centralized server as an intermediary that receives, processes, and coordinates video data from multiple distributed devices. The server manages timestamp correlation, player tracking across devices, and video synthesis, acting as a mediator that simplifies the complexity of synchronizing multiple video sources. This intermediary architecture allows multiple devices to operate independently while maintaining coherent synchronized output.
4Productivity
If automated character recognition is used to identify players, then time consumption is reduced, but identification accuracy may decrease
Solution Approach 1:
The system replaces manual player identification with automated computer vision and character recognition technologies. The system uses image processing to detect and recognize jersey numbers, player positions, and movements automatically, achieving high-speed identification without sacrificing accuracy. Machine learning models are trained to accurately distinguish players even in challenging conditions such as partial occlusions or varying image qualities.
Solution Approach 2:
The system incorporates feedback mechanisms where the automated identification results are continuously refined based on tracking consistency and action context. The system cross-validates player identification across multiple video frames and devices, using feedback from successful identifications to improve future recognition accuracy. This iterative feedback process ensures high identification speed while maintaining and improving accuracy over time.
Data Source
AI summary
A computer-implemented method may include receiving at least three video clips of a sporting event, where each of the video clips may (i) be simultaneously captured over at least a portion of time, and (ii) include at least one common player wearing an indicia on a jersey that is distinguishing from indicia on other players. Tracking locations of the at least one common player captured in the at least three video clips may be generated by triangulating distances of the common player(s) in the video clips. Statistical information of the common player(s) may be generated from the tracking locations. The common player(s) may be represented on a graphical display. The common player(s) may be controlled by applying at least one of the tracking locations and statistical information of the common player(s).