首页> Journal of imaging> YOLO-I3D: Optimizing Inflated 3D Models for Real-Time Human Activity Recognition.

YOLO-I3D: Optimizing Inflated 3D Models for Real-Time Human Activity Recognition.

导出 LinkOut

翻译打印收藏纠错

第一作者： Ruikang,Luo

第一单位： School of Computing, Queen's University, Kingston, ON K7L 2N8, Canada.

作者： Ruikang,Luo ^[1] ; Aman,Anand ^[1] ; Farhana,Zulkernine ^[1] ; Francois,Rivest ^[2]

作者单位： School of Computing, Queen's University, Kingston, ON K7L 2N8, Canada. ^[1] School of Computing, Queen's University, Kingston, ON K7L 2N8, Canada.;Royal Military College of Canada, Queen's University, Kingston, ON K7L 2N8, Canada. ^[2]

关键词 Inflated 3D ConvNet (I3D)YOLOv5 convolutional neural network (CNN)human activity recognition (HAR)

DOI 10.3390/jimaging10110269

PMID 39590733

发布时间 2024-11-28

翻译满意度评价：

准确一般字义偏差语意偏离译文作用小提交

浏览0

Journal of imaging

2024年10卷11期

相似文献

中文期刊
外文期刊
学位论文
会议论文

浏览0

点击下载本期目录封面

Journal of imaging

2024年10卷11期

检索历史清除

YOLO-I3D: Optimizing Inflated 3D Models for Real-Time Human Activity Recognition.

Journal of imaging

相似文献

同项目论文

Journal of imaging

相关期刊

参考文献：
指定格式：	NoteExpress EndNote RefWorks NoteFirst

检索历史 清除

YOLO-I3D: Optimizing Inflated 3D Models for Real-Time Human Activity Recognition.

Journal of imaging

相似文献

同项目论文

Journal of imaging

相关期刊

检索历史清除