The Influence of Faulty Labels in Data Sets on Human Pose Estimation

TL;DR

This paper empirically demonstrates that label inaccuracies in human pose estimation datasets significantly affect model performance, highlighting the importance of data quality for developing more robust HPE models.

Contribution

It provides an in-depth analysis of label errors in popular datasets and shows that cleansing data improves model accuracy.

Findings

Label inaccuracies negatively impact HPE model performance.

Cleaning datasets leads to measurable performance improvements.

Faulty labels distort evaluation metrics in HPE.

Abstract

In this study we provide empirical evidence demonstrating that the quality of training data impacts model performance in Human Pose Estimation (HPE). Inaccurate labels in widely used data sets, ranging from minor errors to severe mislabeling, can negatively influence learning and distort performance metrics. We perform an in-depth analysis of popular HPE data sets to show the extent and nature of label inaccuracies. Our findings suggest that accounting for the impact of faulty labels will facilitate the development of more robust and accurate HPE models for a variety of real-world applications. We show improved performance with cleansed data.