A Gesture Recognition Method Based on Interval Distribution Probability Features

Abstract

The present invention relates to a gesture recognition method based on interval distribution probability features, comprising: S1, using infrared sensing technology to collect infrared reflection signals and infrared temperature signals of gestures at a set frequency f; S2, judging whether the infrared temperature difference changes Target action; S3, automatic detection of gesture movement segments, and denoising and normalization, interpolation and normalization processing to obtain new data segments; S4, feature extraction, and at the same time, use standard gesture raw data to establish gesture feature templates as training Sample; S5, at last, utilize KNN method to identify test sample gesture type, the method of the present invention is simple and intuitive, unsupervised learning process, high to the recognition rate of common dynamic gesture, real-time performance is good, and to the change of environmental factor or gesture process The difference is inclusive and has good robustness.

Description

technical field [0001] The present invention relates to the technical field of human-computer interaction, and more specifically, relates to a gesture recognition method based on interval distribution probability features. Background technique [0002] Gesture recognition technology is currently a research hotspot in the field of human-computer interaction and has broad application prospects. Due to the polysemy diversity of gesture itself and the differences in time and space, gesture recognition technology still has a lot of room for development. Traditional gesture recognition mainly includes contact based on wearable sensors and non-contact based on computer vision. The recognition method based on wearable sensors requires users to wear professional equipment, is not affected by changes in the external environment, has a small amount of calculation, and has better real-time performance, but has disadvantages such as inconvenient use and high cost. Although the recogniti...

AI Technical Summary

Problems solved by technology

The recognition method based on wearable sensors requires users to wear professional equipment, is not affected by changes in the external environment background, has a small amount of calculation, and has better real-time performance, but has disadvantages such as inconvenient use and high cost.

Although the recognition method based on computer vision solves the problem of scope of application, it has strict requirements on equipment and environment, and the algorithm is more complicated and the real-time performance is poor.

[0003] At present, there are mainly recognition methods such as template matching, feature extraction, neural network and hidden Markov model for gesture recognition, which are widely adopted, but these recognition algorithms are relatively complex or require large storage space. With the increase of gesture recognition, the system storage space is not enough Or the recognition speed is too slow to affect the interaction efficiency, and it is difficult to meet the requirements of online recognition

Images