Gesture detection method based on multi-feature fusion

A technology of multi-feature fusion and gesture detection, applied in instruments, character and pattern recognition, computer parts, etc., can solve the problems of high hardware cost, poor real-time performance, and poor human-computer interaction ability.

Inactive Publication Date: 2015-01-07
XI AN JIAOTONG UNIV
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Problems solved by technology

However, due to the high difficulty of modeling multi-eye vision, the large amount of calculation and the relatively high hardware cost, monocular gesture recognition is

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  • Gesture detection method based on multi-feature fusion
  • Gesture detection method based on multi-feature fusion
  • Gesture detection method based on multi-feature fusion

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Embodiment Construction

[0027] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0028] The present invention includes gesture classifier training and gesture detection two parts, as Figure 4 It is a schematic diagram of the training process of the gesture classifier (namely Adaboost).

[0029] First, common gestures under different lighting and distance conditions are used as positive samples, and a large number of indoor and outdoor pictures with low repeatability are selected as negative samples to establish a gesture set;

[0030] The present invention establishes a gesture sample set suitable for daily life, including 3000 positive sample pictures, 45000 negative sample pictures and 10000 test set pictures. The positive sample image is formed by superimposing the gesture image and the background image. Gesture pictures include three basic gestures of daily fingers close together, fingers spread apart, and ...

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Abstract

The invention provides a gesture detection method based on multi-feature fusion. The method includes the steps that a cascaded Gentle Adaboost classifier is trained according to a method of fusion of the HOG feature, the variance feature and the Haar feature, so that a gesture classifier is formed; a skin color pre-examination is conducted through a skin color pre-examination module on an image collected by a camera and regions with the color similar to the skin color are screened out; the skin color regions which are screened out are traversed according to a sliding window method, and the input image considered to contain gestures is calibrated through rectangular frames; repeated rectangular region windows which are repeatedly judged as candidate gesture regions multiple times by the classifier are combined, and therefore the gesture of the image is calibrated. The method of fusion of the HOG feature, the variance feature and the Haar feature is selected for training the classifier and the image is measured through various feature values according to multiple peculiarities of the hands, so that the representing performance of the human hands is improved and the accuracy of a detection system is improved.

Description

technical field [0001] The invention belongs to the field of human-computer interaction and machine vision, and relates to a method for human-computer interaction using gesture detection, in particular to a gesture detection method based on multi-feature fusion. Background technique [0002] In the field of human-computer interaction, the current common method is to interact through external devices, such as keyboards, remote controls, and touch screens. In contrast, the gesture recognition method has lower requirements on the user, and has a higher degree of flexibility, which is more suitable for daily life. [0003] Gesture recognition can be divided into two ways according to different input data: one, gesture recognition based on gloves; two, gesture recognition based on vision. The glove-based method requires users to wear special data gloves. Although the accuracy rate is high, it still fails to meet the requirements of natural interaction, and the cost is relatively...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/20G06F18/24G06F18/254G06F18/253G06F18/214
Inventor 梅魁志徐璐王方李博良林斌高增辉王阳
Owner XI AN JIAOTONG UNIV
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