Robust Human Identity Anonymization using Pose Estimation
This work addresses the need for more automated anonymization in autonomous systems, though it appears incremental by building on existing pose estimation methods.
The paper tackled the problem of robust human identity anonymization for outdoor autonomous platforms by using skeleton data from pose estimation to localize human heads, reducing missed faces and better protecting pedestrian identity information.
Many outdoor autonomous mobile platforms require more human identity anonymized data to power their data-driven algorithms. The human identity anonymization should be robust so that less manual intervention is needed, which remains a challenge for current face detection and anonymization systems. In this paper, we propose to use the skeleton generated from the state-of-the-art human pose estimation model to help localize human heads. We develop criteria to evaluate the performance and compare it with the face detection approach. We demonstrate that the proposed algorithm can reduce missed faces and thus better protect the identity information for the pedestrians. We also develop a confidence-based fusion method to further improve the performance.