# Comparison of Visual Trackers for Biomechanical Analysis of Running

arXiv: https://arxiv.org/abs/2505.04713 · HTML: https://mlsanity.com/paper/2505.04713/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 7 May 2025.

## Builds on (most cited first, 11 of 11)

- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [CoTracker: It is Better to Track Together](https://mlsanity.com/paper/2307.07635/) (2023)
- [RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose](https://mlsanity.com/paper/2303.07399/) (2023)
- [PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model](https://mlsanity.com/paper/1803.08225/) (2018)
- [Face Image Quality Assessment: A Literature Survey](https://mlsanity.com/paper/2009.01103/) (2020)
- [Smartphone Sensors for Modeling Human-Computer Interaction: General Outlook and Research Datasets for User Authentication](https://mlsanity.com/paper/2006.00790/) (2020)
- [Quality Measures in Biometric Systems](https://mlsanity.com/paper/2111.08704/) (2021)
- [Exploiting Multiple Representations: 3D Face Biometrics Fusion with Application to Surveillance](https://mlsanity.com/paper/2504.18886/) (2025)
- [Exploring 3D Face Reconstruction and Fusion Methods for Face Verification: A Case-Study in Video Surveillance](https://mlsanity.com/paper/2409.10481/) (2024)
- [VideoRun2D: Cost-Effective Markerless Motion Capture for Sprint Biomechanics](https://mlsanity.com/paper/2409.10175/) (2024)

## Built on by (most cited first, 2 of 2)

- [Is It Really You? Exploring Biometric Verification Scenarios in Photorealistic Talking-Head Avatar Videos](https://mlsanity.com/paper/2508.00748/) (2025)
- [VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Running](https://mlsanity.com/paper/2608.19480/) (2026)

## Abstract (arXiv)

Human pose estimation has witnessed significant advancements in recent years, mainly due to the integration of deep learning models, the availability of a vast amount of data, and large computational resources. These developments have led to highly accurate body tracking systems, which have direct applications in sports analysis and performance evaluation. This work analyzes the performance of six trackers: two point trackers and four joint trackers for biomechanical analysis in sprints. The proposed framework compares the results obtained from these pose trackers with the manual annotations of biomechanical experts for more than 5870 frames. The experimental framework employs forty sprints from five professional runners, focusing on three key angles in sprint biomechanics: trunk inclination, hip flex extension, and knee flex extension. We propose a post-processing module for outlier detection and fusion prediction in the joint angles. The experimental results demonstrate that using joint-based models yields root mean squared errors ranging from 11.41deg to 4.37deg. When integrated with the post-processing modules, these errors can be reduced to 6.99deg and 3.88deg, respectively. The experimental findings suggest that human pose tracking approaches can be valuable resources for the biomechanical analysis of running. However, there is still room for improvement in applications where high accuracy is required.