Abstract

The introduction of Artificial Intelligence to sports began when programmers were hard at work developing algorithms for this new generation of computing. To begin, Artificial Intelligence refers to technology that emulates human tasks, often using machine learning as the method to learn from data how to emulate these tasks.

Overall, AI is considered to be a breakthrough in improving people's quality of life and implementing fitness regimes that are easily operable.

Problem

In a race, participants are given RFID timing chips to produce precise results. These ultra-high frequency tags are ideal for perfectly evaluating the result with no delay. Although these tags make the whole process easier, there might occur a situation where it gets tough to determine whether the participant is the same person who has registered and engaged all the while. Another case includes checking whether the registered participant is the one who crosses every lap on the track. This might be perceived as a teensy bit of an action, but it is capable enough to create huge chaos. Such impersonation can lead to inaccurate results, which might consume more time and energy in evaluating from scratch.

Solution

Racing Events have thousands of participants and are complicated for individual tracking. Here AI is used to collect footage and identify each runner based on their Bib numbers. With the help of effective sensors and algorithms of AI, everything is set under control along with a broad scope of implementations from time to time.

The proposed solution includes the development of AI algorithms with which the imposter situations are detected and so results are produced on the move, without having to wait until the event ends. This method essentially uses computer vision methods to capture real-time data creating scope to recognize a participant faster. Up-to-date participant timings are provided on our website which can be accessed with the Bib numbers.

Pre-Race Preparation

Collect participant data: Gather information such as names, ages, and photographs of registered participants.

Assign unique identifiers: Issue individualized bib numbers or RFID tags to each.

Build a comprehensive database: Create a database containing participant information and their respective identifiers.

AI-Based Tracking System

Computer vision technology: Utilize AI algorithms to analyze live video feeds from strategically placed cameras along the race route.

Facial recognition: Employ facial recognition algorithms to match participants' faces in real-time with their registered photographs.

RFID technology: Integrate RFID readers at various checkpoints to track participants wearing RFID tags.

Data processing: Implement robust data processing pipelines to handle the large volume of data generated during the race.

Conclusion

As a rapidly developing technology, AI will affect more and more industries in the future. This AI-based tracking system significantly reduces the chances of impersonation by accurately matching participants' faces with their registered photographs along with unique identifier verification. Implementation of this AI-based system into races is an effective solution to mitigate impersonation and enhance the overall integrity, security, and efficiency of the event. By leveraging computer vision, facial recognition, and RFID technology, race organizers can accurately identify and track participants in real-time.