Image Analysis Timing System for Race Identification
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Solution Overview
Problem
Existing methods for determining when an individual passes a particular location, such as a finish line in races, face challenges like scalability issues and high costs with manual timing and RFID technology, which can be inaccurate due to radio interference and require extensive equipment and participant tagging.
Innovation Solution
A method using machine learning software components, like artificial neural networks, trained with images and temporal data to identify individuals and determine their proximity to a location, replacing traditional timing methods by analyzing images and extracting temporal information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RFID technology is used for timing, then timing capability is provided, but radio interference causes inaccuracy and extensive equipment tagging is required
Solution Approach 1:
The patent replaces RFID (electromagnetic/radio-based) timing systems with a computer vision-based image analysis system. Instead of using radio frequency identification tags and readers susceptible to radio interference, the system uses cameras to capture images and machine learning algorithms to identify individuals and determine timing, thereby eliminating the harmful effect of radio interference on measurement accuracy.
Solution Approach 2:
The patent uses image copies (photographs) of individuals as the basis for identification and timing, rather than relying on RFID tags. The visual information captured in images serves as the data source, creating a non-contact, interference-free method for individual identification and event timing.
2Productivity
If manual timing devices are used, then timing can be obtained, but scalability is difficult for large events
Solution Approach 1:
The patent implements an automated system where the computer vision and machine learning components perform identification and timing operations autonomously without requiring manual intervention. The system processes images, identifies individuals, and determines timing automatically, enabling the system to handle large numbers of participants simultaneously and scale to large events without proportionally increasing operational complexity.
Solution Approach 2:
The patent replaces manual timing operations with automated computer vision-based timing. Instead of human operators using stopwatches or manual recording devices, the system uses image analysis algorithms to automatically capture timing data, dramatically increasing processing capacity and reducing the complexity of coordinating multiple manual timing operations across large events.
3Measurement precision
If RFID technology is used, then timing data can be collected, but expense is high due to equipment and tagging requirements
Solution Approach 1:
The patent replaces expensive RFID tags and reader equipment with standard imaging devices and software-based identification. The system uses visual information that can be captured by conventional cameras, eliminating the need for costly RFID infrastructure. Individual identification is achieved through image analysis rather than requiring physical tags on each participant, significantly reducing both equipment and deployment costs while maintaining timing accuracy.
Data Source
AI summary
The present disclosure provides a method for training a machine learning software component. In a computing system, a plurality of images and a plurality of location tuples are received. Each of the location tuples includes a subject identifier and a temporal identifier. For each of the location tuples, the subject identifier is associated with an image of the plurality of images using the temporal identifier to form a training data set. The machine learning software component is trained with the training data set.