Dynamic gesture segmentation and recognition method based on Hidden Markov Model (HMM)

Abstract

The invention relates to a dynamic gesture segmentation and recognition method based on a Hidden Markov Model (HMM). The method comprises training of the HMM and dynamic gesture segmentation and recognition. According to the method disclosed by the invention, the starting point and ending point of the continuous dynamic gesture can be effectively detected in real time, the real-time property of gesturing to others is further improved, and the normal gesture conversation habit is met, so that the gesture conversation is natural and smooth. Moreover, in combination with weighted processing, the composite gesture sequence is effectively segmented, redundant data is reduced, an effective gesture with great energy in dynamic gestures is further recognized and extracted, the recognition rate of the gesture recognition after gesture segmentation is improved, and further the gesture recognition precision and efficiency are further improved. Moreover, the problem of spatio-temportal difference of continuous dynamic gesture and the problem of gesture segmentation from gesture starting to ending can be effectively solved in a real-time scene.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence and pattern recognition, in particular to a dynamic gesture segmentation recognition method based on a hidden Markov model. Background technique [0002] With the development of mobile phone touch operation and human body tracking and recognition, people have realized that gesture interaction has the advantages of human-centered naturalness, simplicity, and directness. The interactive interface based on human intelligent input is becoming a new technology trend, especially With the rise of new immersive virtual reality devices, various interactive solutions are used to improve the immersive experience, among which gesture interaction is the most concise, direct and natural. [0003] Gesture recognition, as a means of human-computer interaction, has been widely used in scenarios such as augmented reality, virtual reality, and somatosensory games. For these application scenarios, ope...

AI Technical Summary

Problems solved by technology

[0003] Gesture recognition, as a means of human-computer interaction, has been widely used in scenarios such as augmented reality, virtual reality, and somatosensory games. For these application scenarios, operating gestures are randomly embedded in continuous action flows. Many current gesture recognition systems based on vision It is assumed that there is a pause between the various input actions or separate gestures that have been segmented, and there are relatively few application studies in real-time scenarios. Under actual application conditions, it is difficult to locate the beginning and end of gestures with operational meaning in complex gesture streams. Key points; and the same gesture inevitably has temporal and spatial differences due to different execution speeds and different motion ranges, which will have a great impact on the accuracy and robustness of recognition, resulting in difficulties in automatic segmentation, poor real-time performance, and The problem of low gesture recognition rate

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