Human body behavior prediction method based on human body skeleton movement information

Abstract

The present invention discloses a human body behavior prediction method based on human body skeleton movement information, comprising the following steps: calculating a normalized relative orientative feature of each articulation point of each limb by using human body skeleton information extracted from an RGB D image; carrying out dynamic segmentation on a feature sequence by using a segmentation method based on feature sequence potential difference so as to acquire a posture feature sub-sequence and an action feature sub-sequence; extracting a key posture and an atomic action from the posture feature sub-sequence and the action feature sub-sequence, and constructing a multi-layer graph model based on the key posture and the atomic action; extracting a human body sub-behavior pattern contained in the multi-layer graph model, and constructing a context probability statistical model of the human body sub-behavior pattern; and carrying out identification and prediction of the human body sub-behavior pattern. The human body behavior prediction method based on the human body skeleton movement information has strong robustness for body differences, spatial position differences and the like between different individuals, has strong generalization capability for action differences between different individuals in the same category of behaviors, and has strong identification capability for the action similarity between different categories of behaviors.

Description

Technical field [0001] The invention relates to the technical field of intelligent human-computer interaction and intelligent robots, in particular to a human body behavior prediction method based on human skeleton movement information. Background technique [0002] In the 21st century, human beings will enter an aging society. The development of service robots can make up for the serious shortage of young laborers and solve social problems such as family services and medical services in an aging society. The International Federation of Robotics gave a preliminary definition of service robots: a service robot is a semi-autonomous or fully autonomous robot that can perform services that are beneficial to humans, but does not include equipment engaged in production; if the service robot can perform intelligence with people Friendly interaction, and able to engage in some household service work according to people's behavior in daily life, then the application of service robots in t...

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Problems solved by technology

The International Federation of Robotics gives a preliminary definition of service robots: a service robot is a semi-autonomous or fully autonomous robot that can perform services that are beneficial to humans, but does not include equipment engaged in production; if a service robot can be intelligent with humans Friendly interaction, and being able to engage in some household service work according to people's behaviors in daily life, then the application of service robots in the household service industry will surely form new industries and new markets; in the process of service robots engaging in daily life services , human behavior recognition and prediction is the basis of intelligent interaction between humans and service robots, and plays a vital role in improving the autonomy and intelligence of robots; using human behavior recognition and prediction technology to identify and predict people's daily behavior, It can provide theoretical and practical basis for the daily service of service robots, promote the intelligent and friendly interaction and harmonious coexistence between people and service robots, and improve the comfort of people's lives; there are several problems in the known human behavior recognition and prediction algorithms: first, absolutely Most human behavior recognition algorithms are based on low-level features, that is, training and testing classification models directly based on frame-by-frame pose features, which requires a large amount of calculation; second, when calculating the relative position or orientation of joint points, they are all relative to the body Joint points or hip center joint points do not consider the actual motion model of each joint of the human body, resulting in a more complex motion model for each joint point; third, when extracting key posture features, the entire feature sequence is directly clustered, without distinction The static components and dynamic components of the feature sequence lead to limited accuracy of key poses; Fourth, when segmenting the feature sequence, manual segmentation, fixed number segmentation, fixed interval segmentation, or graph theory-based segmentation are used, and the segmentation effect cannot meet the requirements. ; Fifth, even if the features are extracted by limbs, the behavior model is not modeled by limbs, and the different functions of each limb are not considered. Some algorithms do not consider the coexistence of left-handers and right-handers; Layered Perceptual Model of Behavior

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