LOODM: Live Out-Of-Distribution Mitigation During Human Pose Dataset Production

Imbalanced datasets are a common challenge in machine learning, where rare situations can significantly impact model performance, especially in safety critical applications like autonomous driving. Addressing these imbalances during the recording stage of a dataset is crucial to ensure that models generalize well to new data and real-world scenarios. This paper proposes a novel approach to improve pose dataset distribution live during dataset production by detecting and mitigating rare human poses interactively. Our live out-of-distribution mitigation (LOODM) method uses dimensionality reduction to transform high-dimensional human pose data into a low-dimensional latent space, allowing for efficient analysis of human poses. We analyze the latent space using density and distance to identify unique rare poses and visualize them to a recorded human subject, who can then repeat each rare pose until it is better represented in the dataset. As such, our work focuses on addressing dataset imbalances at their point of origin, namely during recording. LOODM demonstrates significant improvements in the performance of a human pose estimation model on rare poses, while also reducing PCKh metric fluctuations between rare and non-rare poses, making it a promising dataset production tool, e.g., for autonomous driving.