What’s Human Pose Estimation

The era of data-driven artificial intelligence has arrived. The quality of data directly affects the effectiveness and intelligence of the model. In this wave of technological change, datasets in various vertical fields are constantly emerging to meet the needs of machine learning in different scenarios. Whether it is computer vision, natural language processing or behavioral analysis, various datasets contain huge commercial value and technical potential.

Human Keypoints Detection, also known as human pose estimation, is a relatively basic task in computer vision and a pre-task for human action recognition, behavior analysis, and human-computer interaction. In general, human body key point detection can be subdivided into single/multi-person key point detection, 2D/3D key point detection, and some algorithms will track key points after completing key point detection, also known as human body. Attitude tracking.

The human body key point detection algorithm can be divided into 2D key point detection and 3D key point detection according to whether it contains 3D depth information. 2D key point detection started earlier and the research is more mature, but in recent years, 3D keypoint detection has attracted a lot of attention.

At present, the common human body key point recognition based on AI technology can generally estimate the information of 14 main key points of the human body in the video or picture, and some can realize the estimation of the coordinates of 16 key points and their visible information. Human body key point recognition mainly recognizes the positions of the human body’s eyebrows, nose, left and right elbows, left and right arms, left and right eyes, left and right ears, neck, left and right ankles, etc. Multiple postures such as standing, sitting, falling to the ground, and climbing movements are estimated, so as to realize the detection and recognition of action postures.

Human skeleton key point detection is one of the basic algorithms of computer vision, and it plays a fundamental role in the research of other related fields of computer vision, such as behavior recognition, person tracking, gait recognition and other related fields. The specific applications are mainly concentrated in intelligent video surveillance, patient monitoring system, human-computer interaction, virtual reality, human animation, smart home, intelligent security, athlete-assisted training and so on.

However, because the human body is quite flexible, various postures and shapes will appear, and a small change in any part of the human body will generate a new posture, and the visibility of its key points is greatly affected by clothing, posture, and perspective. Moreover, it is also faced with the influence of occlusion, lighting, fog and other environments. In addition, there will be obvious visual differences between 2D human body key points and 3D human body key points, and different parts of the body will have a visual shortening effect (foreshortening) , making human skeleton keypoint detection a very challenging task in the field of computer vision.

Nexdata has been committed to providing multi-scene and multi-type human body keypoint data to help our customers break down technical bottlenecks. Nexdata strictly abides by relevant regulations, and the data is collected with proper authorization.

10,000 People — Human Pose Recognition Data

10,000 People — Human Pose Recognition Data. This dataset includes indoor and outdoor scenes.This dataset covers males and females. Age distribution ranges from teenager to the elderly, the middle-aged and young people are the majorities. The data diversity includes different shooting heights, different ages, different light conditions, different collecting environment, clothes in different seasons, multiple human poses. For each subject, the labels of gender, race, age, collecting environment and clothes were annotated. The data can be used for human pose recognition and other tasks.

10,000 People — Re-ID Data in Surveillance Scenes

10,000 People — Re-ID Data in Surveillance Scenes. The data includes indoor scenes and outdoor scenes. The data includes males and females, and the age distribution is from children to the elderly. The data diversity includes different age groups, different time periods, different shooting angles, different human body orientations and postures, clothing for different seasons. For annotation, the rectangular bounding boxes and 15 attributes of human body were annotated. The data can be used for re-id and other tasks.

11,130 People — Re-ID Data in Real Surveillance Scenes

11,130 People — Re-ID Data in Real Surveillance Scenes. The data includes indoor scenes and outdoor scenes. The data includes males and females, and the age distribution is from children to the elderly. The data diversity includes different age groups, different time periods, different shooting angles, different human body orientations and postures, clothing for different seasons. For annotation, the rectangular bounding boxes and 15 attributes of human body were annotated. This data can be used for re-id and other tasks.

18,880 Images of 466 People — 3D Instance Segmentation and 22 Landmarks Annotation Data of Human Body

18,880 Images of 466 People — 3D Instance Segmentation and 22 Landmarks Annotation Data of Human Body. The dataset diversity includes multiple scenes, light conditions, ages, shooting angles, and poses. In terms of annotation, we adpoted instance segmentation annotations on human body. 22 landmarks were also annotated for each human body. The dataset can be used for tasks such as human body instance segmentation and human behavior recognition.

50,356 Images — Human Body Segmentation and 18 Landmarks Data

The data diversity includes multiple scenes, ages, races, poses, and appendages. In terms of annotation, we adpoted segmentation annotations on human body and appendages.18 landmarks were also annotated for each human body. The data can be used for tasks such as human body segmentation and human behavior recognition.

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In the future, data-driven intelligence will profoundly change all industries operation system. To make sure the long-term development of AI technology, high-quality datasets will remain an indispensable basic resource. By continuously optimizing data collection technology, and developing more sophisticated datasets, AI systems will bring more opportunities and challenges for all walks of life.