Dynamic hand gesture recognition method based on self incremental learning of hidden Markov model

Abstract

The invention discloses a dynamic hand gesture recognition method based on self incremental learning of the hidden Markov model. The method includes the following steps that firstly, a hand gesture is detected and tracked; secondly, feature extraction and vector quantization are carried out; thirdly, model training and hand gesture recognition are performed; fourthly, incremental learning is performed. According to the dynamic hand gesture recognition method based on self incremental learning of the hidden Markov model, dynamic hand gesture operation by a hand gesture operator in front of a camera can be accurately recognized, the recognized hand gesture data can be applied to incremental learning of an original model to adjust model parameters. Thus, the original model can dynamically adapt to novel variation generated in future hand gesture data and high adaptability to adjustment and alternation of the hand gesture data can be achieved. Thus, the model can be adjusted continuously along with the hand gesture data and better robustness on the unknown hand gesture recognition in the future is achieved.

Description

technical field [0001] The invention relates to the fields of computer vision, image processing, pattern recognition and the like, in particular to a dynamic gesture recognition method based on hidden Markov model self-incremental learning. Background technique [0002] With the rapid progress and development of science and technology, computer science also takes off rapidly. At present, while the computer field is developing toward higher speed, higher efficiency, and higher computing rate, it is also striding forward toward the field of human-computer interaction that is more convenient, simpler, and more comfortable. [0003] Especially with the hot sales of a series of electronic consumer products such as mobile phones and tablet computers, providing better man-machine interface and facilitating more natural and harmonious communication between people and computers has become a quite potential economic detonation in the computer field. point. [0004] At present, in th...

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Problems solved by technology

[0011] (2) Once the newly identified samples complete the identification task, they will no longer have other functions, and the system cannot adjust the model parameters in real time according to the newly added samples to make it more adaptable to new scenarios

At this time, it will take a lot of time and energy to retrain all samples (including existing training samples and new recognition samples) to adapt to the new situation.

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