Latent Embedding Clustering for Occlusion Robust Head Pose Estimation

arXiv:2403.20251v1 Announce Type: new
Abstract: Head pose estimation has become a crucial area of research in computer vision given its usefulness in a wide range of applications, including robotics, surveillance, or driver attention monitoring. One of the most difficult challenges in this field is managing head occlusions that frequently take place in real-world scenarios. In this paper, we propose a novel and efficient framework that is robust in real world head occlusion scenarios. In particular, we propose an unsupervised latent …

abstract applications arxiv attention become challenges clustering computer computer vision cs.cv driver embedding head monitoring research robotics robust surveillance type vision