Visual-angle-independent video three-dimensional human body posture recognition method

A technology of human body posture and recognition method, which is applied in the field of video 3D human body posture recognition, can solve problems such as easy overfitting, poor generalization ability, and lack of perspective of 3D acquisition data, and achieve strong generalization ability, clear task purpose, strong The effect of camera generalization ability

Active Publication Date: 2020-06-02
SHANGHAI JIAO TONG UNIV
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  • Description
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AI Technical Summary

Benefits of technology

This patented technology allows for independent verification of both the inner and outer dimensions of an object's 3D shape during image processing without requiring any special equipment like cameras. It also improves upon previous methods such as depth from defocus techniques but still requires specific assumptions about how objects appear when viewed through different angles. Overall, this new approach provides technical benefits including improved understanding capabilities, faster response times, stable predictions, and greater overall performance compared to existing systems.

Problems solved by technology

This patented describes how humans are able to recognize their movements through visual sensors like cameras. These movement signals help us interpret what we see around them better than just looking at things alone. They have many benefits including being accurate even when there aren’t enough depth perception (images) available during daylight hours but they may become less effective if too much light enters our eyes later. To solve these issues, researchers developed algorithms called Multi Stage Humanoid Postpose Recognition(MSPR), which involves multiple stage techniques involving different types of sensor systems and machine learning methods.

Method used

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  • Visual-angle-independent video three-dimensional human body posture recognition method
  • Visual-angle-independent video three-dimensional human body posture recognition method
  • Visual-angle-independent video three-dimensional human body posture recognition method

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Embodiment Construction

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0029] Such as figure 1 Shown is a flow chart of the overall structure of a viewing angle-independent video 3D human body gesture recognition method in the present invention, which mainly includes the following three stages: virtual data generation stage, model training stage, unconstrained video reasoning stage, and also includes a training stage A camera-independent two-dimensional detection normalization method used in both stages of reasoning;

[0030] V

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Abstract

The invention relates to a visual-angle-independent video three-dimensional human body posture recognition method. The method comprises the steps: 1, a virtual data generation stage: generating a two-dimensional/three-dimensional data tuple after synthesizing virtual camera parameters based on any human body posture data set containing three-dimensional marks; step 2, a model training stage: utilizing the generated two-dimensional/three-dimensional data tuples to train a modular neural network first module used for obtaining a model with camera visual angle generalization capability and a modular neural network second module used for obtaining a model capable of protecting inter-frame action continuity respectively; and 3, an unconstrained video reasoning stage: for any unconstrained acquired video, predicting by utilizing the multi-module deep neural network obtained by training in the step 2 to obtain a three-dimensional human body posture recognition result. Compared with the priorart, the method is based on a modular neural network combination training method, and the generalization ability of three-dimensional human body posture recognition is effectively improved.

Description

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Claims

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Application Information

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Owner SHANGHAI JIAO TONG UNIV
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