Role Assignment for Sports Formation Retrieval
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Solution Overview
Problem
Vision-based systems for tracking players in team sports face challenges with high-dimensional tracking data, leading to computationally prohibitive analyses, false detections, and missed detections, which result in erroneous results and require tedious manual editing.
Innovation Solution
A method for assigning roles to agents in a group engaging in an activity, involving receiving detection data, defining exemplar formations, calculating cost functions, generating permutations, and assigning roles based on these calculations to improve data analysis efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vision-based systems track players multiple times per second to capture detailed motion data, then measurement precision and reliability are improved, but data dimensionality increases making analysis computationally prohibitive
Solution Approach 1:
The patent extracts only the essential features from high-dimensional tracking data by identifying and tracking player roles and formations. Instead of analyzing all 200,000 frames of location data, the system extracts key formation patterns and role assignments, reducing computational complexity while maintaining analysis accuracy.
Solution Approach 2:
The system changes parameters by transforming raw location data into role-based representations. By defining cost functions that measure deviation from expected role positions and using permutation-based role assignment, the system transforms high-dimensional spatial data into lower-dimensional role classification data that is computationally manageable.
2Reliability
If manual editing is performed to correct false detections and missed detections, then reliability is improved, but loss of time and productivity decrease
Solution Approach 1:
The system performs self-correction by using cost functions to identify and correct false detections and missed detections automatically. The role assignment algorithm inherently filters out erroneous detections by evaluating which detections best fit the expected formation patterns, eliminating the need for manual editing while maintaining high reliability.
Solution Approach 2:
The system implements feedback mechanisms where the cost function continuously evaluates detection quality against expected formation patterns. When detections deviate significantly from expected roles, the system adjusts assignments to minimize cost, automatically correcting errors without human intervention and maintaining detection accuracy over time.
3Measurement precision
If exhaustive analysis of all tracking data is performed to ensure accurate role assignment, then measurement precision is improved, but productivity and loss of time worsen
Solution Approach 1:
The patent segments the analysis process into distinct stages: detection, cost function evaluation, permutation generation, and role assignment. This segmentation allows the system to process data in manageable steps rather than performing exhaustive analysis all at once, improving computational efficiency while maintaining accuracy through systematic progression through each analysis stage.
Solution Approach 2:
The system performs partial analysis by focusing on the most critical aspects of role assignment using cost functions and permutations. Instead of analyzing every possible configuration, it evaluates only the most likely role assignments based on formation patterns, achieving sufficient accuracy without exhaustive computation and significantly improving analysis productivity.
Data Source
AI summary
Approaches are described for formation retrieval. Embodiments receive positional data, across an interval window, including a respective agent trajectory for each agent and an object trajectory for one or more objects. The interval window is partitioned into frames and, at each frame, embodiments calculate a cost of assigning a role to each agent based on one or more exemplar formations. A formation is determined by assigning a role to each agent based on the calculated cost. Each frame of the formation is compared to a corresponding frame of a stored formation, by calculating a distance between a position of each assigned role in the frame and a position of a corresponding role in the stored formation and by comparing the object trajectory for the one or more objects with a corresponding object trajectory in the stored formation. Based on the comparisons, a list of stored formations is generated.