Rehmann, Markus (Reutlingen University), Brunner, Michael (Reutlingen University), Curio, Cristobal (Reutlingen University)

OODM-DS: Out-Of-Distribution Mitigation Based on Dynamic Sampling for Human Pose Dataset Creation

Scheduled for presentation during the Regular Session "S37b-Reliable Perception and Robust Sensing for Intelligent Vehicles" (FR-EA-T37), Friday, November 21, 2025, 14:30−14:50, Coolangata 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Imbalanced datasets pose a significant challenge in machine learning, where rare situations can significantly impact model performance, particularly in safety-critical applications such as autonomous driving. To ensure models generalize well to new data and real-world scenarios, it is essential to address imbalances during the dataset creation. We propose a novel approach to improve the distribution of human pose datasets by detecting and mitigating rare poses in human motion data. Our method, Out-Of-Distribution Mitigation based on Dynamic Sampling (OODM-DS), employs dimensionality reduction to transform high-dimensional human pose data into a low-dimensional latent space, facilitating efficient analysis of human poses. By analyzing the latent space using density estimation, we propose a dynamic sampling strategy to generate a dataset with a connected simulation. This approach significantly improves the performance of a human pose estimation model on rare poses, while also reducing PCKh metric variance between rare and non-rare poses, making it a promising tool for dataset production in applications such as autonomous driving where safety-critical data can be rare and difficult to obtain.