[{"@type":"PropertyValue","name":"Data size","value":"165 people, 32 images were annotated for each subject"},{"@type":"PropertyValue","name":"Population distribution","value":"149 brown people, 15 Asians, 1 Caucasian people"},{"@type":"PropertyValue","name":"Gender distribution","value":"87 males, 78 females"},{"@type":"PropertyValue","name":"Age distribution","value":"18 people under 18 years old, 135 people aged from 18 to 45 years old, 7 people aged from 46 to 50 years old"},{"@type":"PropertyValue","name":"Collecting environment","value":"outdoor scenes"},{"@type":"PropertyValue","name":"Data diversity","value":"different age groups, different time periods, different shooting angles, different human body orientations and postures, different modal cameras"},{"@type":"PropertyValue","name":"Device","value":"binocular surveillance cameras (IR+RGB)"},{"@type":"PropertyValue","name":"Collecting angle","value":"looking down angle"},{"@type":"PropertyValue","name":"Collecting time","value":"night"},{"@type":"PropertyValue","name":"Data format","value":"the image data format is .jpg, the annotation file format is .json"},{"@type":"PropertyValue","name":"Annotation content","value":"human body rectangular bounding boxes, 15 human body attributes; Label the subject’s gender, age, race, nationality, camera ID, camera height, camera modal"},{"@type":"PropertyValue","name":"Acccuracy rate","value":"a rectangular bounding box of human body is qualified when the deviation is not more than 5 pixels, and the qualified rate of the bounding boxes shall not be lower than 95% ; Annotation accuracy of human attributes is over 95%; The accuracy of label annotation is not less than 95%"}]
{"id":1811,"datatype":"1","titleimg":"https://www.nexdata.ai/shujutang/static/image/index/datatang_tuxiang_default.webp","type1":"147","type1str":null,"type2":"148","type2str":null,"dataname":"165 People Night Surveillance Person Re-Identification Dataset (RGB-IR, Attributes)","datazy":[{"title":"Data size","content":"165 people, 32 images were annotated for each subject"},{"title":"Population distribution","content":"149 brown people, 15 Asians, 1 Caucasian people"},{"title":"Gender distribution","content":"87 males, 78 females"},{"title":"Age distribution","content":"18 people under 18 years old, 135 people aged from 18 to 45 years old, 7 people aged from 46 to 50 years old"},{"title":"Collecting environment","content":"outdoor scenes"},{"title":"Data diversity","content":"different age groups, different time periods, different shooting angles, different human body orientations and postures, different modal cameras"},{"title":"Device","content":"binocular surveillance cameras (IR+RGB)"},{"title":"Collecting angle","content":"looking down angle"},{"title":"Collecting time","content":"night"},{"title":"Data format","content":"the image data format is .jpg, the annotation file format is .json"},{"title":"Annotation content","content":"human body rectangular bounding boxes, 15 human body attributes; Label the subject’s gender, age, race, nationality, camera ID, camera height, camera modal"},{"title":"Acccuracy rate","content":"a rectangular bounding box of human body is qualified when the deviation is not more than 5 pixels, and the qualified rate of the bounding boxes shall not be lower than 95% ; Annotation accuracy of human attributes is over 95%; The accuracy of label annotation is not less than 95%"}],"datatag":"Re-id,CCTV,Outdoor Night Scenes","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":null,"samplePresentation":[{"name":"1.png","url":"https://storage-product.datatang.com/damp/product/instructions_zh/20260119162638/1.png?Expires=4102415999&OSSAccessKeyId=LTAI5tEBeSWUJiqjXvBMsxEu&Signature=GgHT0%2BWUL9X1vSnDpkgF3sDF3Vg%3D","intro":"","size":731034,"progress":100,"type":"jpg"},{"name":"2.png","url":"https://storage-product.datatang.com/damp/product/instructions_zh/20260119162638/2.png?Expires=4102415999&OSSAccessKeyId=LTAI5tEBeSWUJiqjXvBMsxEu&Signature=Ial7KGIUaBsSrm%2BgPisMF%2FoEoUg%3D","intro":"","size":612162,"progress":100,"type":"jpg"},{"name":"3.png","url":"https://storage-product.datatang.com/damp/product/instructions_zh/20260119162638/3.png?Expires=4102415999&OSSAccessKeyId=LTAI5tEBeSWUJiqjXvBMsxEu&Signature=y1vJ6VkuVvgkiuralT23lR85fbg%3D","intro":"","size":558341,"progress":100,"type":"jpg"}],"officialSummary":"This dataset comprises 165 individuals, captured in outdoor nighttime surveillance scenarios. It covers a diverse demographic—including both males and females and age groups ranging from children to middle-aged adults, with a predominance of young people—and features both RGB and infrared (IR) image modalities. Annotations include bounding boxes and 15 categories of human attributes, making the data suitable for person re-identification (ReID), multi-camera tracking, pedestrian detection, and surveillance video analysis tasks.","dataexampl":null,"datakeyword":["person re identification dataset","pedestrian reid dataset","reid dataset","surveillance dataset","cctv dataset","video surveillance 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","dataShowType":"[{\"code\":\"0\",\"language\":\"ZH\"},{\"code\":\"1\",\"language\":\"ZH\"},{\"code\":\"2\",\"language\":\"EN,JP\"},{\"code\":\"3\",\"language\":\"EN\"},{\"code\":\"4\",\"language\":\"JP\"}]","productNameEn":"165 People - CCTV Re-ID Data in Outdoor Night Scenes","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":"4.png","url":"https://storage-product.datatang.com/damp/product/instructions_zh/20260119162638/4.png?Expires=4102415999&OSSAccessKeyId=LTAI5tEBeSWUJiqjXvBMsxEu&Signature=K7MlJskJIqGbL1pAjYhu99UC%2BDY%3D","intro":"","size":587642,"progress":100,"type":"jpg"}]}
165 People Night Surveillance Person Re-Identification Dataset (RGB-IR, Attributes)
person re identification dataset
pedestrian reid dataset
reid dataset
surveillance dataset
cctv dataset
video surveillance dataset
This dataset comprises 165 individuals, captured in outdoor nighttime surveillance scenarios. It covers a diverse demographic—including both males and females and age groups ranging from children to middle-aged adults, with a predominance of young people—and features both RGB and infrared (IR) image modalities. Annotations include bounding boxes and 15 categories of human attributes, making the data suitable for person re-identification (ReID), multi-camera tracking, pedestrian detection, and surveillance video analysis tasks.
This is a paid datasets for commercial use, research purpose and more. Licensed ready made datasets help jump-start AI projects.
Specifications
Data size
165 people, 32 images were annotated for each subject
Population distribution
149 brown people, 15 Asians, 1 Caucasian people
Gender distribution
87 males, 78 females
Age distribution
18 people under 18 years old, 135 people aged from 18 to 45 years old, 7 people aged from 46 to 50 years old
Collecting environment
outdoor scenes
Data diversity
different age groups, different time periods, different shooting angles, different human body orientations and postures, different modal cameras
Device
binocular surveillance cameras (IR+RGB)
Collecting angle
looking down angle
Collecting time
night
Data format
the image data format is .jpg, the annotation file format is .json
Annotation content
human body rectangular bounding boxes, 15 human body attributes; Label the subject’s gender, age, race, nationality, camera ID, camera height, camera modal
Acccuracy rate
a rectangular bounding box of human body is qualified when the deviation is not more than 5 pixels, and the qualified rate of the bounding boxes shall not be lower than 95% ; Annotation accuracy of human attributes is over 95%; The accuracy of label annotation is not less than 95%