Robust Human Pose Estimation under Gaussian Noise

Abstract

Robustness against specific kinds of noise is of high importance for safety-critical components in industrial robot applications, as legal and normative regulations demand the identification and handling of all unacceptable risks. This includes risks from environmental conditions, like noisy data. One such component is human pose estimation, which is needed and crucial for human-robot collaboration tasks and applications. However, little research on human pose estimation under specific noise types has been performed. In our work, we focus on extensively evaluating human pose estimation under specific noise and propose potential countermeasures. We leverage Gaussian noise as specific noise type and the hourglass model as human pose estimator. We show that human pose estimation is already vulnerable to small amounts of Gaussian noise. As countermeasures we propose either denoising images upfront or training the hourglass model to be robust against Gaussian noise. All methods achieve a significantly higher robustness against Gaussian noise, typically at the cost of slightly worse performance on clean data. Three of our methods also achieved slight improvements on clean data.