Active headrest combined with a depth camera-based ear-positioning system
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Abstract (arXiv)
Active headrests can reduce low-frequency noise around ears based on active noise control (ANC) system. Both the control system using fixed control filters and the remote microphone-based adaptive control system provide good noise reduction performance when the head is in the original position. However, their performance degrades significantly when the head is in motion. In this paper, a human ear-positioning system based on the depth camera is introduced to address this problem. The system uses RTMpose model to estimate the two-dimensional (2D) positions of the ears in the color frame, and then derives the corresponding three-dimensional (3D) coordinates in the depth frame with a depth camera. Experimental results show that the ear-positioning system can effectively track the movement of ears, and the broadband noise reduction performance of the active headrest combined with the system is significantly improved when the human head is translating or rotating.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 25 Dec 2023.
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References · 9
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,420
- 2017Cascaded Pyramid Network for Multi-Person Pose Estimationcited by 341
- 2022RTMDet: An Empirical Study of Designing Real-Time Object Detectorscited by 195
- 2023RTMPose: Real-Time Multi-Person Pose Estimation based on MMPosecited by 175
- 2017Intel RealSense Stereoscopic Depth Camerascited by 83
- 2017AI Challenger : A Large-scale Dataset for Going Deeper in Image Understandingcited by 73
- 2021SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimationcited by 68
- 2021Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspectivecited by 50
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