Abstract
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.
Abstract (translated)
在超长距离跑步比赛中重新识别参与者可能会让人望而生畏，因为比赛距离广阔，地形不断变化。为克服这些挑战，计算机视觉技术被开发出来分析跑步者的面容、号码和服装。然而，我们的研究提出了一种新颖的基于步态的跑步者重新识别（RE-ID）方法，通过利用各种预训练的人动作识别（HAR）模型和损失函数。我们的研究结果表明，这种方法在超长距离比赛中识别跑步者具有相当大的潜力。此外，我们研究了当运动员接近其极限时，不同的人体运动对重新识别准确度的影响，以及这种影响如何影响跑步者步态识别的准确性。我们的研究探讨了运动员的关键点（CP），即疲劳程度严重时的时刻，距离终点仅有一两公里，此位置为CP。我们旨在确定此CP如何提高运动员重新识别的准确性。我们的实验结果表明，当运动员接近这个点时，步态识别可以显著增强（MAP增加9%）。这表明在现实场景中，如超长距离比赛或长时间监视任务，可以利用步态识别技术。
URL
https://arxiv.org/abs/2401.00080
