Gesture detection method based on multi-feature fusion

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...

AI Technical Summary

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 currently widely used, and the existing monocular gesture recognition methods have poor real-time performance and low accuracy. Poor human-computer interaction ability

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