Gesture identification method and device in depth image

Abstract

The invention relates to a gesture identification method and device in a depth image. The method comprises the following steps of extracting a human body three dimension profile form the depth image; calculating local features of the three dimension profile; inputting the local features of the three dimension profile into a preset human body structure model to obtain the absolute space distribution and condition space distribution of human body joint points; calculating the positions of the human body joint points in a three dimension space according to the absolute space distribution and the condition space distribution of the human body joint points to obtain a human body gesture identification result. On the basis of the original random sen, gesture identification is performed by the preset human body structure model, the method has low feature dimension and strong description capability, human body gestures are uniformly learned aiming at the bodies of different heights by the human body structure model, the learning ability is reduced, and the practicability and accurate rate of an algorithm are improved; compared with the original probabilistic graphical model, the method is faster in gesture reasoning and is suitable for a real-time gesture capturing system.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a gesture recognition method and device in a depth image. Background technique [0002] Gesture recognition is one of the key technologies of human-computer interaction. At present, the method of component recognition is mainly used to identify various parts of the human body, such as limbs, head and other parts, and then connect the various components to form a human body posture. However, only using the results of component detection for gesture recognition often loses a large amount of human body structure information, resulting in unsatisfactory gesture recognition results. A probabilistic graphical model is introduced in the process of building human body poses from components. However, this model has high computational complexity, which is difficult to meet the requirements of real-time applications. In addition, there are two types in terms of image type...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is: in the prior art, a large amount of human body structure information is often lost for posture recognition using the results of component detection, resulting in unsatisfactory posture recognition results. In the process of constructing human body posture by components, a probability graph model is introduced. However, this model has high computational complexity and is difficult to meet the requirements of real-time applications.

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