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{"id":1277,"datatype":"1","titleimg":"/shujutang/static/image/index/datatang_tuxiang_default.webp","type1":"147","type1str":null,"type2":"147","type2str":null,"dataname":"1,000 People Driver Behavior Identification Data","datazy":[{"title":"Data size","value":"1,000 people"},{"title":"Population distribution","value":"gender distribution: male, female; race distribution: Asian; age distribution: 18~45 years old, 46~60 years old, over 60 years old"},{"title":"Collecting environment","value":"in-car Cameras"},{"title":"Data diversity","value":"multiple age periods, multiple time periods, multiple lighting and behaviors (Dangerous behavior, Fatigue behavior, Visual movement behavior)"},{"title":"Device","value":"visible light and infrared binocular camera, resolution 1,920x1,080"},{"title":"Shooting position","value":"the center of the inside rear view mirror of the car, above the center console in the car, above the left A-pillar in the car, steering wheel position"},{"title":"Collecting time","value":"day, evening, night"},{"title":"Collecting light","value":"normal light, weak light, strong light"},{"title":"Vehicle Type","value":"car, SUV, MVP, truck, coach"},{"title":"Data Format","value":"the video data format is .mp4"},{"title":"Accuracy","value":"according to the accuracy of each person's acquisition action, the accuracy exceeds 95%;the accuracy of label annotation is not less than 95%"}],"datatag":"Driver behavior,Dangerous behavior,Fatigue behavior,Visual movement behavior,Multiple ages,Multiple time periods","technologydoc":null,"downurl":null,"datainfo":"","standard":null,"dataylurl":null,"flag":null,"publishtime":null,"createby":null,"createtime":null,"ext1":null,"samplestoreloc":null,"hosturl":null,"datasize":null,"industryPlan":null,"keyInformation":"","samplePresentation":[["jpg","https://bj-oss-datatang-03.oss-cn-beijing.aliyuncs.com/filesInfoUpload/data/apps/damp/temp/ziptemp/APY231031002_demo1711101604117/APY231031002_demo/1.png?Expires=4102329599&OSSAccessKeyId=LTAI8NWs2pDolLNH&Signature=NkdXDNZrQmB%2BN6w4TWE5g0UmPlE%3D","/data/apps/damp/temp/ziptemp/APY231031002_demo1711101604117/APY231031002_demo/1.png",""],["jpg","https://bj-oss-datatang-03.oss-cn-beijing.aliyuncs.com/filesInfoUpload/data/apps/damp/temp/ziptemp/APY231031002_demo1711101604117/APY231031002_demo/4.png?Expires=4102329599&OSSAccessKeyId=LTAI8NWs2pDolLNH&Signature=7c0bWEiwGph%2B7FExUTZf1sjNA9A%3D","/data/apps/damp/temp/ziptemp/APY231031002_demo1711101604117/APY231031002_demo/4.png",""],["jpg","https://bj-oss-datatang-03.oss-cn-beijing.aliyuncs.com/filesInfoUpload/data/apps/damp/temp/ziptemp/APY231031002_demo1711101604117/APY231031002_demo/3.png?Expires=4102329599&OSSAccessKeyId=LTAI8NWs2pDolLNH&Signature=8JkjSdPQlMkuO6FfOqura39A2Yg%3D","/data/apps/damp/temp/ziptemp/APY231031002_demo1711101604117/APY231031002_demo/3.png",""]],"officialSummary":"1,000 People-Driver Behavior Identification Data. The data includes multiple ages, multiple time periods and multiple lighting. The driver behaviors includes Dangerous behavior, fatigue behavior and visual movement behavior. In terms of device, binocular cameras of RGB and infrared channels were applied. This data can be used for tasks such as driver behavior analysis.","dataexampl":"","datakeyword":["Driver behavior acquisition data"," driver behavior acquisition"," driver data acquisition"," driver behavior data"],"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"],"single":"no","firstList":[["jpg","https://bj-oss-datatang-03.oss-cn-beijing.aliyuncs.com/filesInfoUpload/data/apps/damp/temp/ziptemp/APY231031002_demo1711101604117/APY231031002_demo/5.png?Expires=4102329599&OSSAccessKeyId=LTAI8NWs2pDolLNH&Signature=Jok0UYx3Ip79meFWEHBn5dc8Pm8%3D","/data/apps/damp/temp/ziptemp/APY231031002_demo1711101604117/APY231031002_demo/5.png",""]]}
1,000 People-Driver Behavior Identification Data. The data includes multiple ages, multiple time periods and multiple lighting. The driver behaviors includes Dangerous behavior, fatigue behavior and visual movement behavior. In terms of device, binocular cameras of RGB and infrared channels were applied. This data can be used for tasks such as driver behavior analysis.
This is a paid datasets for commercial use, research purpose and more. Licensed ready made datasets help jump-start AI projects.
Specifications
Data size
1,000 people
Population distribution
gender distribution: male, female; race distribution: Asian; age distribution: 18~45 years old, 46~60 years old, over 60 years old
Collecting environment
in-car Cameras
Data diversity
multiple age periods, multiple time periods, multiple lighting and behaviors (Dangerous behavior, Fatigue behavior, Visual movement behavior)
Device
visible light and infrared binocular camera, resolution 1,920x1,080
Shooting position
the center of the inside rear view mirror of the car, above the center console in the car, above the left A-pillar in the car, steering wheel position
Collecting time
day, evening, night
Collecting light
normal light, weak light, strong light
Vehicle Type
car, SUV, MVP, truck, coach
Data Format
the video data format is .mp4
Accuracy
according to the accuracy of each person's acquisition action, the accuracy exceeds 95%;the accuracy of label annotation is not less than 95%