Computer Vision Datasets

This dataset includes 3,010 images of 602 people across multiple races. The semantic segmentation area includes headphones, glasses, body and background.This dataset can be used for training AI models in human body segmentation, video conference behavior detection, and smart vision applications.

This dataset contains 50,356 human images, The data covers diverse scenes, ages, races, poses, and appendages.In terms of annotation, each human body have 18 landmarks, with segmentation for the body and appendages. This dataset can be used for tasks such as human pose estimation, body part segmentation, action recognition.

This dataset contains 568 individuals, ethnic groups include East Asians, Caucasians, Blacks, and Browns, primarily young adults. Various indoor office scenes were captured, including meeting rooms, cafes, libraries, and bedrooms. In terms of annotation, each individual has 61 to 64 photos with annotations for detected facial bounding boxes, 106 facial landmarks, and segmentation annotations for the human body. This dataset can be used for tasks like face detection, facial landmark recognition, human body segmentation, and computer vision applications in virtual meeting or office environments.

3,360 Images of 168 people - passenger behavior human body detection data. The data scenes are the In-cabin camera scenes. The gender distribution is male and female, and the age distribution is mainly young and middle-aged. Data diversity includes multiple age groups, multiple time periods, multiple lights, and multiple passenger behaviors. In terms of content collection, passengers' normal behavior, calling behavior and smoking behavior are collected. In terms of labeling, the human rectangular box is labeled. The data can be used for tasks such as human body detection of passenger behavior. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

4,290 Images – Occluded Human Body Collection Data. There are 2-5 occluded human bodies for each images. The age distribution is ranging from children to the elderly, the middle-aged and young people are the majorities. The race distribution is Asian. The data diversity includs multiple scenes, multiple age groups, multiple occluded between human bodies, multiple human body postures. The data can be used for tasks such as occluded human body detection and segmentation.

208,914 Bounding Boxes – Human Body Attributes Data in Surveillance Scenes. The data includes indoor (shopping mall) and outdoor (street, the gate of shopping mall and square) scenes. The data includes males and females and the age distribution is from children to the elderly. In this dataset, the rectangular bounding boxes and 19 attributes of human body were annotated. The data can be used for person attributes recognition.

34,981 Images – Alpha Matte Human Body Segmentation Data. The data includes indoor scenes and outdoor scenes. The dataset diversity includes multiple scenes, multiple age groups, multiple human body angles, multiple postures. In terms of annotation, alpha matte segmentation annotation was adopted for the human body. The data can be used for tasks such as alpha matte human body segmentation.

25,080 Images – Alpha Matte Human Body Segmentation Data. The data includes indoor scenes and outdoor scenes. The dataset diversity includes multiple scenes, multiple age groups, multiple human body angles, multiple postures. In terms of annotation, alpha matte segmentation annotation was adopted for the human body. The data can be used for tasks such as alpha matte human body segmentation.

21,300 Images - Human Body Segmentation Data. The data includes indoor scenes and outdoor scenes. The data covers female people and male people. The race distribution includes Asian, black race and Caucasian. The age distribution ranges from teenager to the elderly, the middle-aged and young people are the majorities. The dataset diversity includes multiple scenes, ages, races, postures, and appendages. In terms of annotation, we adpoted pixel-wise segmentation annotations on human body. The data can be used for tasks such as human body segmentation.