Effect of Obfuscation on Human Pose Estimation Models: Can We Balance Privacy and Accuracy?

Human pose estimation (HPE) models based on RGB images are widely used in applications such as surveillance, sports analytics, and healthcare. However, they often overlook privacy requirements mandated by data protection laws worldwide. One approach to ensure privacy is to use alternative data types such as LiDAR, WiFi signals, or depth images, but these can result in reduced accuracy compared to RGB-based models. A practical alternative is to apply obfuscation techniques to RGB images, which can help preserve privacy while retaining the benefits of high-resolution visual data. This paper investigates how HPE models perform under different types and intensities of image obfuscation. We focus on two common methods: Gaussian blur and pixelation, applied at multiple intensity levels. We evaluated three state-of-the-art HPE models, HRNet-W48, ViTPose Base, and RTMPose-l, in the COCO dataset, analyzing their robustness against obfuscated images. Key questions include how performance degrades with increasing obfuscation, which body parts are most affected, and the obfuscation thresholds where model fine-tuning becomes necessary. To complement this performance evaluation, we also show heatmaps to explain how obfuscation impacts the model focus on different body parts. Our study shows how to balance privacy and accuracy in HPE systems, offering practical guidance on using obfuscation techniques with existing RGB models to enhance privacy while maintaining performance without retraining.