https://scholars.lib.ntu.edu.tw/handle/123456789/632255
標題: | Activity recognition using first-person-view cameras based on sparse optical flows | 作者: | Kao P.-Y Lei Y.-J Chang C.-H Chen C.-S Lee M.-S YI-PING HUNG |
公開日期: | 2020 | 起(迄)頁: | 81-86 | 來源出版物: | Proceedings - International Conference on Pattern Recognition | 摘要: | First-person-view (FPV) cameras are finding wide use in daily life to record activities and sports. In this paper, we propose a succinct and robust 3D convolutional neural network (CNN) architecture accompanied with an ensemble-learning network for activity recognition with FPV videos. The proposed 3D CNN is trained on low-resolution (32 ? 32) sparse optical flows using FPV video datasets consisting of daily activities. According to the experimental results, our network achieves an average accuracy of 90%. © 2020 IEEE |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110484950&doi=10.1109%2fICPR48806.2021.9412330&partnerID=40&md5=32c36c338722fa087a1d459b8057a86b https://scholars.lib.ntu.edu.tw/handle/123456789/632255 |
ISSN: | 10514651 | DOI: | 10.1109/ICPR48806.2021.9412330 | SDG/關鍵字: | Cameras; Convolutional neural networks; Optical flows; Activity recognition; Daily activity; Daily lives; Ensemble learning; First person; Low resolution; Video datasets; Pattern recognition |
顯示於: | 資訊工程學系 |
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