Abstract

Race timers can now be produced smaller than ever. Adjusted into a wristband, clothing shoes, or object, an RFID timing system can be used to collect data on speed, rest, and race times. It has been approved that RFID can add value and visibility to racing events of all types. One of the long-established applications of RFID has been marathon racing. The tracking device uses passive RFID technology with Antennas built into specifically designed mats that the athletes cross during the marathon. It records the net time of the participants all along the course.

Problem

Before the introduction of RFID into the racing sector, performance calculation and tracking of the time of each participant was manually executed. It has shown many complications and the odds of a result being uncorrupted are uneven.

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.