Active headrest combined with a depth camera-based ear-positioning system (2401.10256v1)
Abstract: 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.
- M De Diego and A Gonzalez, “Performance evaluation of multichannel adaptive algorithms for local active noise control,” Journal of Sound and vibration, vol. 244, no. 4, pp. 615–634, 2001.
- “Active noise control for headrests,” in 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). Ieee, 2015, pp. 688–692.
- “Broadband performance of an active headrest,” The journal of the Acoustical Society of America, vol. 106, no. 2, pp. 787–793, 1999.
- “Electronic sound absorber,” The Journal of the Acoustical Society of America, vol. 25, no. 6, pp. 1130–1136, 1953.
- “Active cancellation of pressure at a point in a pure tone diffracted diffuse sound field,” Journal of sound and vibration, vol. 201, no. 1, pp. 43–65, 1997.
- “Performance evaluation of an active headrest considering non-stationary broadband disturbances and head movement,” The Journal of the Acoustical Society of America, vol. 143, no. 5, pp. 2571–2579, 2018.
- “Head tracking extends local active control of broadband sound to higher frequencies,” Scientific reports, vol. 8, no. 1, pp. 5403, 2018.
- “Combining the remote microphone technique with head-tracking for local active sound control,” The Journal of the Acoustical Society of America, vol. 142, no. 1, pp. 298–307, 2017.
- “Multi-functional active noise control system on headrest of airplane seat,” Mechanical Systems and Signal Processing, vol. 167, pp. 108552, 2022.
- “Combination of robust algorithm and head-tracking for a feedforward active headrest,” Applied Sciences, vol. 9, no. 9, pp. 1760, 2019.
- “Head movement immune active noise control with head mounted moving microphones,” The Journal of the Acoustical Society of America, vol. 142, no. 2, pp. 573–587, 2017.
- “Ultra-broadband local active noise control with remote acoustic sensing,” Scientific reports, vol. 10, no. 1, pp. 20784, 2020.
- “Recent advances of monocular 2d and 3d human pose estimation: A deep learning perspective,” ACM Computing Surveys, vol. 55, no. 4, pp. 1–41, 2022.
- “Cascaded pyramid network for multi-person pose estimation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7103–7112.
- “Deep high-resolution representation learning for visual recognition,” IEEE transactions on pattern analysis and machine intelligence, vol. 43, no. 10, pp. 3349–3364, 2020.
- “Vitpose: Simple vision transformer baselines for human pose estimation,” Advances in Neural Information Processing Systems, vol. 35, pp. 38571–38584, 2022.
- “Rtmpose: Real-time multi-person pose estimation based on mmpose,” arXiv preprint arXiv:2303.07399, 2023.
- “Rtmdet: An empirical study of designing real-time object detectors,” arXiv preprint arXiv:2212.07784, 2022.
- “Simcc: A simple coordinate classification perspective for human pose estimation,” in European Conference on Computer Vision. Springer, 2022, pp. 89–106.
- “Microsoft coco: Common objects in context,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. Springer, 2014, pp. 740–755.
- “2d human pose estimation: New benchmark and state of the art analysis,” in Proceedings of the IEEE Conference on computer Vision and Pattern Recognition, 2014, pp. 3686–3693.
- “Ai challenger: A large-scale dataset for going deeper in image understanding,” arXiv preprint arXiv:1711.06475, 2017.
- “Depth camera technology comparison and performance evaluation.,” in ICPRAM (2), 2012, pp. 438–444.
- “Intel realsense stereoscopic depth cameras,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017, pp. 1–10.
- Stephen Elliott, Signal processing for active control, Elsevier, 2000.