2D Image head pose estimation via latent space regression under occlusion settings

• URL: http://arxiv.org/abs/2311.06038v1
• Date: Fri, 10 Nov 2023 12:53:02 GMT
• Title: 2D Image head pose estimation via latent space regression under occlusion settings
• Authors: Jos\'e Celestino, Manuel Marques, Jacinto C. Nascimento and Jo\~ao Paulo Costeira
• Abstract summary: The strategy is based on latent space regression as a fundamental key to better structure the problem for occluded scenarios. We demonstrate the usefulness of the proposed approach with: (i) two synthetically occluded versions of the BIWI and AFLW2000 datasets, (ii) real-life occlusions of the Pandora dataset, and (iii) a real-life application to human-robot interaction scenarios.
• Score: 7.620379605206596
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Head orientation is a challenging Computer Vision problem that has been extensively researched having a wide variety of applications. However, current state-of-the-art systems still underperform in the presence of occlusions and are unreliable for many task applications in such scenarios. This work proposes a novel deep learning approach for the problem of head pose estimation under occlusions. The strategy is based on latent space regression as a fundamental key to better structure the problem for occluded scenarios. Our model surpasses several state-of-the-art methodologies for occluded HPE, and achieves similar accuracy for non-occluded scenarios. We demonstrate the usefulness of the proposed approach with: (i) two synthetically occluded versions of the BIWI and AFLW2000 datasets, (ii) real-life occlusions of the Pandora dataset, and (iii) a real-life application to human-robot interaction scenarios where face occlusions often occur. Specifically, the autonomous feeding from a robotic arm.

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