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3D (three-dimensional) Gaussian space human behavior identifying method based on image depth information

A technology of Gaussian space and image depth, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as the inability to guarantee the accuracy of human behavior recognition, errors, etc.

Active Publication Date: 2014-05-21
重庆微铭汇信息技术有限公司
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Problems solved by technology

[0004] However, the 3D joints estimated based on monocular depth information have a lot of noise and even obvious errors, especially in the case of occlusion, such as crossed hands, multiple human bodies touching each other, etc.
Based on this 3D joint reasoning, the accuracy of human behavior recognition is still not guaranteed

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  • 3D (three-dimensional) Gaussian space human behavior identifying method based on image depth information
  • 3D (three-dimensional) Gaussian space human behavior identifying method based on image depth information
  • 3D (three-dimensional) Gaussian space human behavior identifying method based on image depth information

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Embodiment Construction

[0067] Such as figure 1 , 2 As shown, in step 1, for the depth information of each frame, the single-pixel object recognition method based on the random decision forest classifier proposed by Shotton is used to confirm the human body parts and further obtain the 3D joint coordinates of the human body. The left picture is the depth image, and the right picture is the above method The corresponding bone image obtained.

[0068] Such as figure 2 , 3 As shown, in step 2, normalizing the 3D joint coordinate data of the human body includes normalizing the size of the skeleton limb vector, normalizing the skeleton reference zero point and normalizing the skeleton direction.

[0069] Wherein the step of normalizing the vector size of the skeleton limbs comprises:

[0070] a) Select a human body 3D joint coordinates as the standard model;

[0071] b) keep the vector direction of each sample limb segment unchanged, and scale each vector to the standard model length;

[0072] c) C...

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Abstract

The invention discloses a 3D (three-dimensional) Gaussian space human behavior identifying method based on image depth information. The 3D Gaussian space human behavior identifying method based on the image depth information includes the steps: extracting human skeleton 3D coordinates in the depth information, normalizing the 3D coordinates and filtering joints with low human behavior identifying rate and redundant joints; building interest joint groups according to behaviors, checking human motion space characteristics based on Gaussian distance to perform AP (affinity propagation) clustering to obtain word lists of behavior characteristics, and performing data cleaning for the word lists; building human behavior conditional random field identifying models, and classifying the human behaviors according to the human behavior conditional random field identifying models. The 3D Gaussian space human behavior identifying method has high interference resistance for specific directions, skeleton sizes and space positions of a human body, high generalization capability for motion differences led in by different experiment individuals and excellent identifying capability for inhomogeneous similar behaviors.

Description

Technical field: [0001] The invention relates to the field of machine vision, in particular to a 3D Gaussian space human behavior recognition method based on image depth information. Background technique: [0002] Human behavior recognition in video has important applications in many fields such as video surveillance, human-computer interaction, and video restoration. Although in the past ten years, experts and scholars from various countries have proposed many methods and made a lot of exciting progress in this field, high-precision human behavior recognition is still a very challenging task. One of the reasons is that human behavior is a dynamic time sequence of actions, and the boundaries of various actions are blurred. Even the same person's actions will be deformed, and even various actions are combined with each other. At the same time, the situation of being blocked may occur during the action. occur. The segmentation of the human body itself from the background is ...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 蒋敏孔军唐晓微姜克郑宪成
Owner 重庆微铭汇信息技术有限公司
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