# An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment

arXiv: https://arxiv.org/abs/2307.12183 · HTML: https://mlsanity.com/paper/2307.12183/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 22 references that are in the map.
- First posted on arXiv 22 Jul 2023.

## Builds on (most cited first, 17 of 17)

- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment](https://mlsanity.com/paper/1904.04346/) (2019)
- [Learning To Score Olympic Events](https://mlsanity.com/paper/1611.05125/) (2016)
- [Uncertainty-aware Score Distribution Learning for Action Quality Assessment](https://mlsanity.com/paper/2006.07665/) (2020)
- [Action Quality Assessment Across Multiple Actions](https://mlsanity.com/paper/1812.06367/) (2018)
- [Group-aware Contrastive Regression for Action Quality Assessment](https://mlsanity.com/paper/2108.07797/) (2021)
- [Learning Spatiotemporal Features with 3D Convolutional Networks](https://mlsanity.com/paper/1412.0767/) (2014)
- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [Auto-Encoding Score Distribution Regression for Action Quality Assessment](https://mlsanity.com/paper/2111.11029/) (2021)
- [A Survey of Video-based Action Quality Assessment](https://mlsanity.com/paper/2204.09271/) (2022)
- [Towards cumulative race time regression in sports: I3D ConvNet transfer learning in ultra-distance running events](https://mlsanity.com/paper/2208.11191/) (2022)
- [Decontextualized I3D ConvNet for ultra-distance runners performance analysis at a glance](https://mlsanity.com/paper/2203.06749/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Decade of Action Quality Assessment: Largest Systematic Survey of Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2502.02817/) (2025)

## Abstract (arXiv)

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.