An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment
This work addresses performance assessment for athletes in wild sporting environments, representing an incremental improvement with specific efficiency gains.
The researchers tackled the problem of estimating runners' cumulative race times in ultra-distance competitions using X3D neural networks, achieving a mean absolute error of 12.5 minutes for footage of runners active 8-20 hours. Their method also required seven times less memory than previous approaches while achieving state-of-the-art performance.
We present a transfer learning analysis on a sporting environment of the expanded 3D (X3D) neural networks. Inspired by action quality assessment methods in the literature, our method uses an action recognition network to estimate athletes' cumulative race time (CRT) during an ultra-distance competition. We evaluate the performance considering the X3D, a family of action recognition networks that expand a small 2D image classification architecture along multiple network axes, including space, time, width, and depth. We demonstrate that the resulting neural network can provide remarkable performance for short input footage, with a mean absolute error of 12 minutes and a half when estimating the CRT for runners who have been active from 8 to 20 hours. Our most significant discovery is that X3D achieves state-of-the-art performance while requiring almost seven times less memory to achieve better precision than previous work.