five

University of Washington Indoor Object Manipulation (UW IOM) Dataset

收藏
NIAID Data Ecosystem2026-03-12 收录
下载链接:
https://data.mendeley.com/datasets/xwzzkxtf9s
下载链接
链接失效反馈
官方服务:
资源简介:
The University of Washington Indoor Object Manipulation (UW IOM) dataset comprises videos (and corresponding skeletal tracking information) of twenty participants within the age group of 18-25 years, of which fifteen are males and the remaining five are females. The videos are recorded using a Kinect Sensor for Xbox One at an average rate of twelve frames per second. Each participant carries out the same set of tasks in terms of picking up six objects (three identical empty boxes and three identical rods) from three different vertical racks, placing them on a table, putting them back on the racks from where they are picked up, and then walking out of the scene carrying the box from the middle rack. The boxes are manipulated with both the hands while the rods are manipulated using only one hand. The above tasks are repeated in the same sequence three times such that the duration of every video is approximately three minutes. We categorize the actions into seventeen labels, where each label follows a four-tier hierarchy. The first tier indicates whether the box or the rod is manipulated, the second tier denotes human motion (walk, stand, and bend), the third tier captures the type of object manipulation if applicable (reach, pick-up, place, and hold), and the fourth tier represents the relative height of the surface where manipulation is taking place (low, medium, and high).

华盛顿大学室内物体操作(University of Washington Indoor Object Manipulation, UW IOM)数据集涵盖20名年龄介于18至25岁参与者的视频及对应骨骼追踪信息,其中男性15名,女性5名。该数据集的视频采用适用于Xbox One的Kinect传感器录制,平均帧率为12帧每秒。每名参与者需完成一套固定操作任务:从三个不同的立式货架上拾取6件物品(3个相同空箱与3根相同杆件),将其放置于桌面,随后将物品放回原拾取货架,最后携带中间货架的箱子离开场景。操作箱子需使用双手,操作杆件则仅需单手。上述任务按相同顺序重复三次,每条视频的时长约为3分钟。我们将动作划分为17个标签,每个标签均遵循四层层级结构:第一层用于区分操作对象为箱子还是杆件;第二层表示人体动作类型(行走、站立与弯腰);第三层记录适用的物体操作类型(伸手、拾取、放置与握持);第四层代表操作所在表面的相对高度(低、中、高)。
创建时间:
2020-11-02
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作