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A Dynamic Gesture Recognition Method Based on Feature Covariance Matrix

A technology of covariance matrix and dynamic gestures, applied in character and pattern recognition, instruments, calculations, etc., can solve problems such as tracking, model training template matching efficiency, etc., achieve easy implementation, reduce tracking requirements, improve training efficiency and overall The effect of gestures

Active Publication Date: 2020-08-18
SOUTH CHINA UNIV OF TECH
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AI Technical Summary

Problems solved by technology

However, so far, few studies have been done on dynamic gesture recognition based on hand joint features.
[0006] It can be seen that the existing algorithms in the field of dynamic gesture recognition still have many shortcomings, especially the hand tracking, model training and template matching efficiency in two-dimensional dynamic gesture recognition.

Method used

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  • A Dynamic Gesture Recognition Method Based on Feature Covariance Matrix
  • A Dynamic Gesture Recognition Method Based on Feature Covariance Matrix
  • A Dynamic Gesture Recognition Method Based on Feature Covariance Matrix

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Embodiment

[0064] Such as Figure 1 to Figure 4 As shown, this embodiment provides a dynamic gesture recognition method based on a feature covariance matrix, which solves the problem of hand tracking and matching efficiency in two-dimensional dynamic gesture recognition, uses the joint characteristics of the hand to realize three-dimensional dynamic gesture recognition, and is a two-dimensional dynamic gesture recognition method. Gesture recognition and 3D dynamic gesture recognition provide a unified framework, including the following four steps:

[0065] S1. Use the skin color information and the pyramid LK optical flow algorithm to track the two-dimensional gestures in the RGB video sequence to obtain the FAST corner points of the hand area in each frame; or use Intel's RealSense F200 camera to capture 3D dynamic gestures to obtain each frame The three-dimensional position of the joint points of the human hand in the camera coordinate system;

[0066] Wherein, in step S1, the skin co...

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Abstract

The present invention proposes a dynamic gesture recognition method based on a feature covariance matrix, which provides a unified framework for two-dimensional and three-dimensional dynamic gesture recognition; for two-dimensional dynamic gesture recognition, only the hand area in three consecutive frames It is not necessary to accurately track the entire hand, which greatly reduces the tracking requirements and is easier to implement; for 3D dynamic gesture recognition, use the SDK that comes with RealSense F200 to real-time Obtaining the 3D skeleton information of the human hand and obtaining the 3D coordinates of the joint points of the hand in each frame can robustly track gestures; the logarithmic covariance matrix descriptor provides a compact representation for the entire gesture sequence, greatly improving the The training efficiency of the SVM model and the efficiency of the whole gesture recognition system.

Description

technical field [0001] The invention relates to the technical field of image processing and analysis, in particular to a dynamic gesture recognition method based on a feature covariance matrix. Background technique [0002] Vision-based gesture recognition plays an extremely important role in human-computer interaction, robot control, virtual reality and other fields, so it has received extensive attention in the field of computer vision. Generally speaking, gestures can be divided into two types: static gestures and dynamic gestures. Static gesture recognition mainly recognizes hand gestures from a single image, so it is largely limited to practical application scenarios. Compared with static gestures, dynamic gestures include the movement process of the hand and can convey more information. In addition, dynamic gestures are generally collected in a moving environment, making dynamic gesture recognition more widely used in application scenarios. [0003] Dynamic gestures...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/28G06F18/2411G06F18/214
Inventor 康文雄方林普吴桂乐
Owner SOUTH CHINA UNIV OF TECH
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