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In the progress of constructing intelligent system, the quality of the training datasets are more important than algorithm itself. For coping with different challenges in complex scenarios, researchers need to collect and annotate different types of data to improve the capabilities of AI system. Nowadays, every industries are exploring constantly how to use data-driven technology to realize smarter business processes and decision-making systems.

The task of human behavior recognition aims to identify the specific actions of the human body by analyzing the posture of the human body, and provide technical support for human behavior prediction, emergency handling, smart fitness, smart nursing and other fields.

Labeling Method for Human Behavior Recognition Data

Common labeling methods for human behavior data include human body key point labeling and action label labeling. Human body key point labeling provides position information of each joint point of the human body. According to different recognition accuracy requirements, the key points of the human body can usually be marked with 14 points, 18 points, 22 points or even more points, all of which are based on the expansion of movable joint points in the human skeleton. Label annotation mainly marks the behavior category corresponding to the action, which is an overall description of human behavior. Human behavior is usually divided into static behavior and dynamic behavior, and the specific labeling forms are as follows:

Static Behavior: directly mark the key points of the target human body in the image, and mark the overall behavior type.

Dynamic Behavior: For dynamic behavior, it is also necessary to add a video frame extraction module to extract image frames based on a specific sampling rate for dynamic human behavior videos, and then mark the key points of the human body on the image frames, and mark the human body behavior type labels on the dynamic behavior videos as a whole.

Difficulties in Human Behavior Recognition Tasks

Based on the actual situation, the task of human behavior recognition has the following three difficulties:

Complex behaviors: There are many types of human behaviors, and some behaviors have a certain degree of subjectivity (such as wandering behavior), which makes it difficult for algorithms to identify them.

Human body occlusion: The human body will occlude itself when performing specific behaviors. In addition, the human body will be occluded by other objects in the scene in a real scene, making it difficult to detect key points.

Data noise: In real scenarios, data noise has a greater impact. Taking security monitoring scenarios as an example, firstly, the resolution of security cameras is generally low, the shooting distance is long, and the proportion of human body features is small; Features have a greater impact; in addition, when the human body makes dynamic movements, it will also cause motion blur.

The above factors pose challenges to the robustness of human body recognition algorithms.

Nexdata Human Behavior Recognition Data

According to the task requirements and difficulties of human behavior recognition, Nexdata has developed a series of data sets for static behavior and dynamic behavior respectively from the data level.

● 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.

● 5,521 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.

● 65 People and 15204 Fitness Videos Human Behavior Recognition Data

This data set uses color cameras and infrared cameras to collect human body fitness video data. In order to ensure the diversity of data, various designs have been made for shooting distance and collection clothing during the collection process. In terms of specific fitness behaviors, the data covers 50 common fitness movements, including rope skipping, push-ups, planks, vertical jumps, squats, jacking jumps, etc., which can meet the needs of most fitness behavior recognition algorithms. In terms of data labeling, the dataset labels the fitness action labels corresponding to each video.

Relying on its own data advantages and rich experience in data processing, Nexdata will be committed to empowering for the extensive application of human behavior recognition technology.

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Standing at the forefront of technology revolution, we are well aware of the power of data. In the future, through contentiously improve data collection and annotation process, AI system will become more intelligent. All walks of life should actively embrace the innovation of data-driven to stay ahead in the fierce market competition and bring more value for society.