Latent Embedding Clustering for Occlusion Robust Head Pose Estimation
This work addresses head occlusions in real-world applications like robotics and surveillance, offering an incremental improvement over existing methods.
The paper tackles the problem of head pose estimation under occlusion by proposing an unsupervised latent embedding clustering framework with regression and classification components, achieving competitive performance on benchmark datasets with significant data reduction.
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 embedding clustering with regression and classification components for each pose angle. The model optimizes latent feature representations for occluded and non-occluded images through a clustering term while improving fine-grained angle predictions. Experimental evaluation on in-the-wild head pose benchmark datasets reveal competitive performance in comparison to state-of-the-art methodologies with the advantage of having a significant data reduction. We observe a substantial improvement in occluded head pose estimation. Also, an ablation study is conducted to ascertain the impact of the clustering term within our proposed framework.