Pre-race Movement Analysis for Racer Prediction
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Solution Overview
Problem
Users, especially non-experts, find it difficult to effectively analyze pre-race movement images of racehorses or motorboats to make informed predictions or purchase decisions due to variability in assessment criteria and the need to examine multiple images.
Innovation Solution
An information processing device with a racer identification unit, attention point identification unit, and presentation controller that identifies key attention points in pre-race movement images based on racing results, providing users with guided information to focus on specific characteristics of racehorses or motorboats during their pre-race movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users examine pre-race movement images to make predictions, then prediction accuracy may improve, but the time and effort required increases significantly
Solution Approach 1:
The system extracts and highlights only the critical attention points from pre-race movement images, separating the essential predictive information from the rest of the image data. This allows users to focus on specific key features rather than examining entire images, significantly reducing analysis time while maintaining prediction accuracy.
Solution Approach 2:
The system segments pre-race movement images into multiple attention points based on racer type and historical performance data. Each attention point represents a specific feature or behavior that is predictive of racing outcomes, allowing users to systematically evaluate multiple aspects without being overwhelmed by the complexity of analyzing complete images.
2Reliability
If users check all pre-race movement images of entered racers, then comprehensive assessment is achieved, but the complexity and trouble of the task increases
Solution Approach 1:
The system performs preliminary analysis of pre-race movement images to identify and categorize attention points before user review. By pre-processing images to extract meaningful features and organize them by racer type and significance, the system reduces the complexity of user assessment while ensuring comprehensive coverage of all relevant racers.
Solution Approach 2:
The system acts as an intermediary between raw pre-race movement images and user decision-making. It processes images to generate structured attention point data that bridges the gap between complex visual information and user-friendly prediction tools, simplifying the assessment process while maintaining reliability.
3Ease of operation
If assessment criteria are standardized for all racers, then ease of evaluation increases, but the precision of individual racer assessment decreases
Solution Approach 1:
The system applies local quality by customizing attention points according to racer type (e.g., horse, motorboat) and individual characteristics. Each racer receives a tailored set of attention points based on their specific features and historical performance, allowing for precise individual assessment while maintaining a standardized system framework that ensures ease of operation.
Data Source
AI summary
To make predictions about racing, a point to pay attention to in pre-race movements of each racer can be presented. To this end, for racers entered in a race to be processed, a plurality of captured pre-race movement images of pre-race movements made by the racers before a race are retrieved. By using the retrieved pre-race movement images and racing result information corresponding to each pre-race movement image, an attention point to be paid attention to while each racer is making pre-race movements are identified. Presentation information for presenting information about the identified attention point is then generated and controlled to be presented to a user on an external terminal.