https://scholars.lib.ntu.edu.tw/handle/123456789/581309
標題: | Multiple-image super-resolution for networked extremely low-resolution thermal sensor array | 作者: | CHI-SHENG SHIH YUN-TUNG WANG Chou J.-J. |
關鍵字: | Machine learning; Activity recognition; Effective solution; Evaluation results; Higher resolution; Human activities; Long-term trend; State of the art; Thermal sensors; Wearable sensors | 公開日期: | 2020 | 起(迄)頁: | 1-6 | 來源出版物: | Proceedings - 2020 IEEE 2nd Workshop on Machine Learning on Edge in Sensor Systems, SenSys-ML 2020 | 摘要: | Observing activeness of patients and elderly for long time at private space is a desired metric for elderly care. Many state-of-The-Art mechanisms require either using wearable sensors, assists by medical staffs, or observation at designated location. Consequently, it is difficult, if not impossible, to acquire long term trend. Thermal sensors are not sensitive to illumination and can detect heat sources under low lightness. Moreover, it is not trivial to identify an individual from thermal images. However, a low cost and effective solution remain open. Low-resolution thermal sensors cost less but suffer from noisy reading. In this work, we fuse multiple low-resolution thermal images to reconstruct higher resolution thermal images to identify human activities. The evaluation results show that the accuracy of activity recognition while using low resolution thermal images are 96% of that while using high resolution thermal images, which requires 80 times of data and ten times of hardware cost. ? 2020 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092705324&doi=10.1109%2fSenSysML50931.2020.00004&partnerID=40&md5=7a86bf62954eb8ff46948b4c7f31e2f1 https://scholars.lib.ntu.edu.tw/handle/123456789/581309 |
DOI: | 10.1109/SenSysML50931.2020.00004 |
顯示於: | 資訊工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。