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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.19404 |
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| _version_ | 1866909603262889984 |
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| author | Yang, Xuefeng Zhang, Shiheng Guan, Jian Xiao, Feiyang Lu, Wei Zhu, Qiaoxi |
| author_facet | Yang, Xuefeng Zhang, Shiheng Guan, Jian Xiao, Feiyang Lu, Wei Zhu, Qiaoxi |
| contents | This study is based on the ICASSP 2025 Signal Processing Grand Challenge's Accelerometer-Based Person-in-Bed Detection Challenge, which aims to determine bed occupancy using accelerometer signals. The task is divided into two tracks: "in bed" and "not in bed" segmented detection, and streaming detection, facing challenges such as individual differences, posture variations, and external disturbances. We propose a spectral-temporal fusion-based feature representation method with mixup data augmentation, and adopt Intersection over Union (IoU) loss to optimize detection accuracy. In the two tracks, our method achieved outstanding results of 100.00% and 95.55% in detection scores, securing first place and third place, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19404 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spectral-Temporal Fusion Representation for Person-in-Bed Detection Yang, Xuefeng Zhang, Shiheng Guan, Jian Xiao, Feiyang Lu, Wei Zhu, Qiaoxi Signal Processing Computer Vision and Pattern Recognition Machine Learning This study is based on the ICASSP 2025 Signal Processing Grand Challenge's Accelerometer-Based Person-in-Bed Detection Challenge, which aims to determine bed occupancy using accelerometer signals. The task is divided into two tracks: "in bed" and "not in bed" segmented detection, and streaming detection, facing challenges such as individual differences, posture variations, and external disturbances. We propose a spectral-temporal fusion-based feature representation method with mixup data augmentation, and adopt Intersection over Union (IoU) loss to optimize detection accuracy. In the two tracks, our method achieved outstanding results of 100.00% and 95.55% in detection scores, securing first place and third place, respectively. |
| title | Spectral-Temporal Fusion Representation for Person-in-Bed Detection |
| topic | Signal Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2412.19404 |