graviti
产品服务
解决方案
知识库
公开数据集
关于我们
Online RGBD Action Dataset (ORGBD)
Person
|...
许可协议: Unknown

Overview

The Online RGBD Action dataset targets for human aciton (human-object interaction) recognition based on RGBD video data. There are seven categories of human actions: Drinking, eating, using laptop, reading cellphone, making phone call, reading book, using remote.The dataset is designed for three evaluation tasks (S# refers to the folder name):1. same-evnironment aciton recognition (two-fold validation, 1) S1 for training and S2 for testing, 2) S2 for training and S1 for testing)2. cross-environment action recognition (S1+S2 for training, S3 for testing)3. continuous action recognition (S1+S2+S0 for training, S4 for testing)The naming scheme for video file is as follow:a#i_s#j_e#k:------------------------------------------------------------------#i refers to the action category index#i = 0 - 6: Drinking, eating, using laptop, reading cellphone, making phone call, reading book, using remote#i = 8: long video sequence (contains multiple actions)#i = 10: negative action (without any of the predifined action)------------------------------------------------------------------#j refers to the subject index------------------------------------------------------------------#k refers to the episode index------------------------------------------------------------------Download:link:http://pan.baidu.com/s/1o80Q3QMpassword:15ejDiscriminative Orderlet Mining For Real-time Recognition of Human-Object InteractionGang Yu, Zicheng Liu, Junsong YuanAsian Conference on Computer Vision (ACCV) 2014

数据概要
数据格式
image,
数据量
--
文件大小
--
| 数据量 -- | 大小 --
Online RGBD Action Dataset (ORGBD)
Person
许可协议: Unknown

Overview

The Online RGBD Action dataset targets for human aciton (human-object interaction) recognition based on RGBD video data. There are seven categories of human actions: Drinking, eating, using laptop, reading cellphone, making phone call, reading book, using remote.The dataset is designed for three evaluation tasks (S# refers to the folder name):1. same-evnironment aciton recognition (two-fold validation, 1) S1 for training and S2 for testing, 2) S2 for training and S1 for testing)2. cross-environment action recognition (S1+S2 for training, S3 for testing)3. continuous action recognition (S1+S2+S0 for training, S4 for testing)The naming scheme for video file is as follow:a#i_s#j_e#k:------------------------------------------------------------------#i refers to the action category index#i = 0 - 6: Drinking, eating, using laptop, reading cellphone, making phone call, reading book, using remote#i = 8: long video sequence (contains multiple actions)#i = 10: negative action (without any of the predifined action)------------------------------------------------------------------#j refers to the subject index------------------------------------------------------------------#k refers to the episode index------------------------------------------------------------------Download:link:http://pan.baidu.com/s/1o80Q3QMpassword:15ejDiscriminative Orderlet Mining For Real-time Recognition of Human-Object InteractionGang Yu, Zicheng Liu, Junsong YuanAsian Conference on Computer Vision (ACCV) 2014

0
立即开始构建AI
graviti
wechat-QR
长按保存识别二维码,关注Graviti公众号

Copyright@Graviti
沪ICP备19019574号
沪公网安备 31011002004865号