  Re-identifying participants in ultra-distance running competitions can be
daunting due to the extensive distances and constantly changing terrain. To
overcome these challenges, computer vision techniques have been developed to
analyze runners' faces, numbers on their bibs, and clothing. However, our study
presents a novel gait-based approach for runners' re-identification (re-ID) by
leveraging various pre-trained human action recognition (HAR) models and loss
functions. Our results show that this approach provides promising results for
re-identifying runners in ultra-distance competitions. Furthermore, we
investigate the significance of distinct human body movements when athletes are
approaching their endurance limits and their potential impact on re-ID
accuracy. Our study examines how the recognition of a runner's gait is affected
by a competition's critical point (CP), defined as a moment of severe fatigue
and the point where the finish line comes into view, just a few kilometers away
from this location. We aim to determine how this CP can improve the accuracy of
athlete re-ID. Our experimental results demonstrate that gait recognition can
be significantly enhanced (up to a 9% increase in mAP) as athletes approach
this point. This highlights the potential of utilizing gait recognition in
real-world scenarios, such as ultra-distance competitions or long-duration
surveillance tasks.
