[{"@type":"PropertyValue","name":"Data size","value":"21,404 images, one images includes one human body"},{"@type":"PropertyValue","name":"Population distribution","value":"gender distribution: male, female; age distribution: ranging from young to the elderly, the middle-aged and young people are the majorities; race distribution: Asian"},{"@type":"PropertyValue","name":"Collecting environment","value":"101 different indoor hone scenes"},{"@type":"PropertyValue","name":"Data diversity","value":"multiple scenes, multiple time periods, multiple collecting heights, multiple human body occlusions, multiple collecting distances"},{"@type":"PropertyValue","name":"Device","value":"surveillance camera, the resolution is 1,920*1,080 or 2,560*1,920"},{"@type":"PropertyValue","name":"Collecting angle","value":"looking down angle"},{"@type":"PropertyValue","name":"Collecting height","value":"1 meter, 1.5 meters, 2 meters"},{"@type":"PropertyValue","name":"Collecting time","value":"day, night"},{"@type":"PropertyValue","name":"Collecting time","value":"the image data format is .jpg, the annotation file format is .json or .xml"},{"@type":"PropertyValue","name":"Collection content","value":"collecting the human body postures data in different home scenes, the human bodies were lying flat, lying on its side or lying on its stomach"},{"@type":"PropertyValue","name":"Annotation content","value":"human body rectangular bounding boxes were annotated"},{"@type":"PropertyValue","name":"Accuracy rate","value":"the rectangular bounding box of human body is qualified when the deviation is not more than 3 pixels, and the qualified rate of the bounding boxes shall not be lower than 97%"}]
{"id":1348,"datatype":"1","titleimg":"https://www.nexdata.ai/shujutang/static/image/index/datatang_tuxiang_default.webp","type1":"147","type1str":null,"type2":"148","type2str":null,"dataname":"21,404 Images – In-Home Human Posture & Activity Recognition Dataset","datazy":[{"title":"Data size","desc":"Data size","content":"21,404 images, one images includes one human body"},{"title":"Population distribution","desc":"Population distribution","content":"gender distribution: male, female; age distribution: ranging from young to the elderly, the middle-aged and young people are the majorities; race distribution: Asian"},{"title":"Collecting environment","desc":"Collecting environment","content":"101 different indoor hone scenes"},{"title":"Data diversity","desc":"Data diversity","content":"multiple scenes, multiple time periods, multiple collecting heights, multiple human body occlusions, multiple collecting distances"},{"title":"Device","desc":"Device","content":"surveillance camera, the resolution is 1,920*1,080 or 2,560*1,920"},{"title":"Collecting angle","desc":"Collecting angle","content":"looking down angle"},{"title":"Collecting height","desc":"Collecting height","content":"1 meter, 1.5 meters, 2 meters"},{"title":"Collecting time","desc":"Collecting time","content":"day, night"},{"title":"Collecting time","desc":"Collecting time","content":"the image data format is .jpg, the annotation file format is .json or .xml"},{"title":"Collection content","desc":"Collection content","content":"collecting the human body postures data in different home scenes, the human bodies were lying flat, lying on its side or lying on its stomach"},{"title":"Annotation content","desc":"Annotation content","content":"human body rectangular bounding boxes were annotated"},{"title":"Accuracy rate","desc":"Accuracy rate","content":"the rectangular bounding box of human body is qualified when the deviation is not more than 3 pixels, and the qualified rate of the bounding boxes shall not be lower than 97%"}],"datatag":"Multiple scenes,Multiple time periods,Multiple collecting heights,Multiple human body occlusions,Multiple collecting distances","technologydoc":null,"downurl":null,"datainfo":null,"standard":null,"dataylurl":null,"flag":null,"publishtime":null,"createby":null,"createtime":null,"ext1":null,"samplestoreloc":null,"hosturl":null,"datasize":null,"industryPlan":null,"keyInformation":"","samplePresentation":[{"name":"/data/apps/damp/temp/ziptemp/APY230908001_demo1711015210990/APY230908001_demo/1.png","url":"https://bj-oss-datatang-03.oss-cn-beijing.aliyuncs.com/filesInfoUpload/data/apps/damp/temp/ziptemp/APY230908001_demo1711015210990/APY230908001_demo/1.png?Expires=4102329599&OSSAccessKeyId=LTAI8NWs2pDolLNH&Signature=VGifUhH2tz3GuGeVY4wAZE1r9jo%3D","intro":"","size":0,"progress":100,"type":"jpg"},{"name":"/data/apps/damp/temp/ziptemp/APY230908001_demo1711015210990/APY230908001_demo/2.jpg","url":"https://bj-oss-datatang-03.oss-cn-beijing.aliyuncs.com/filesInfoUpload/data/apps/damp/temp/ziptemp/APY230908001_demo1711015210990/APY230908001_demo/2.jpg?Expires=4102329599&OSSAccessKeyId=LTAI8NWs2pDolLNH&Signature=LVxEHF%2B96d%2Fy0KHFli0sk2BKmcU%3D","intro":"","size":0,"progress":100,"type":"jpg"},{"name":"/data/apps/damp/temp/ziptemp/APY230908001_demo1711015210990/APY230908001_demo/4.png","url":"https://bj-oss-datatang-03.oss-cn-beijing.aliyuncs.com/filesInfoUpload/data/apps/damp/temp/ziptemp/APY230908001_demo1711015210990/APY230908001_demo/4.png?Expires=4102329599&OSSAccessKeyId=LTAI8NWs2pDolLNH&Signature=0NmUEILuK7tRt%2BokNztdW%2FNMxQ8%3D","intro":"","size":0,"progress":100,"type":"jpg"}],"officialSummary":"The 21,404 Images – In-Home Human Posture & Activity Recognition Dataset captures diverse in-home environments across 101 unique indoor scenes. The gender distribution includes male and female, age distribution ranging from young to the elderly, the middle-aged and young people are the majorities. The data diversity includes multiple scenes, multiple time periods, multiple collecting heights, multiple human body occlusions, multiple collecting distances. For collection content, the human body postures data in different home scenes were collected. Collected postures include lying flat, lying on the side, and lying on the stomach, annotated with bounding boxes. The data can be used for tasks such as human body detection, activity recognition, fall detection, elderly care monitoring, smart home surveillance, and healthcare AI applications.","dataexampl":null,"datakeyword":["human posture detection dataset","in-home human activity dataset","smart home monitoring dataset","lying down human detection dataset","home surveillance AI dataset","human lying posture dataset","abnormal posture detection dataset"],"isDelete":null,"ids":null,"idsList":null,"datasetCode":null,"productStatus":null,"tagTypeEn":"Task Type,Modalities","tagTypeZh":null,"website":null,"samplePresentationList":null,"datazyList":null,"keyInformationList":null,"dataexamplList":null,"bgimg":null,"datazyScriptList":null,"datakeywordListString":null,"sourceShowPage":"computer","BGimg":"","voiceBg":["/shujutang/static/image/comm/audio_bg.webp","/shujutang/static/image/comm/audio_bg2.webp","/shujutang/static/image/comm/audio_bg3.webp","/shujutang/static/image/comm/audio_bg4.webp","/shujutang/static/image/comm/audio_bg5.webp"],"firstList":[{"name":"/data/apps/damp/temp/ziptemp/APY230908001_demo1711015210990/APY230908001_demo/3.png","url":"https://bj-oss-datatang-03.oss-cn-beijing.aliyuncs.com/filesInfoUpload/data/apps/damp/temp/ziptemp/APY230908001_demo1711015210990/APY230908001_demo/3.png?Expires=4102329599&OSSAccessKeyId=LTAI8NWs2pDolLNH&Signature=npS7DtWIthJPzRU3fpwtGZ2aHQs%3D","intro":"","size":0,"progress":100,"type":"jpg"}]}
21,404 Images – In-Home Human Posture & Activity Recognition Dataset
human posture detection dataset
in-home human activity dataset
smart home monitoring dataset
lying down human detection dataset
home surveillance AI dataset
human lying posture dataset
abnormal posture detection dataset
The 21,404 Images – In-Home Human Posture & Activity Recognition Dataset captures diverse in-home environments across 101 unique indoor scenes. The gender distribution includes male and female, age distribution ranging from young to the elderly, the middle-aged and young people are the majorities. The data diversity includes multiple scenes, multiple time periods, multiple collecting heights, multiple human body occlusions, multiple collecting distances. For collection content, the human body postures data in different home scenes were collected. Collected postures include lying flat, lying on the side, and lying on the stomach, annotated with bounding boxes. The data can be used for tasks such as human body detection, activity recognition, fall detection, elderly care monitoring, smart home surveillance, and healthcare AI applications.
This is a paid datasets for commercial use, research purpose and more. Licensed ready made datasets help jump-start AI projects.
Specifications
Data size
21,404 images, one images includes one human body
Population distribution
gender distribution: male, female; age distribution: ranging from young to the elderly, the middle-aged and young people are the majorities; race distribution: Asian
Collecting environment
101 different indoor hone scenes
Data diversity
multiple scenes, multiple time periods, multiple collecting heights, multiple human body occlusions, multiple collecting distances
Device
surveillance camera, the resolution is 1,920*1,080 or 2,560*1,920
Collecting angle
looking down angle
Collecting height
1 meter, 1.5 meters, 2 meters
Collecting time
day, night
Collecting time
the image data format is .jpg, the annotation file format is .json or .xml
Collection content
collecting the human body postures data in different home scenes, the human bodies were lying flat, lying on its side or lying on its stomach
Annotation content
human body rectangular bounding boxes were annotated
Accuracy rate
the rectangular bounding box of human body is qualified when the deviation is not more than 3 pixels, and the qualified rate of the bounding boxes shall not be lower than 97%
Specifications
Sample
Recommended Dataset
