Instruction identification method and electronic equipment

Abstract

The invention discloses an instruction identification method and electronic equipment. The instruction identification method is applied to the electronic equipment; the electronic equipment comprises an information acquiring unit and a keyboard; the instruction identification method comprises the steps that at least one part of the keyboard is divided into at least two sub zones, the sub zones are encoded, and the keyboard division result and the coding of each sub zone are stored; the information acquiring unit captures the coverage situation of each sub zone; element values are assigned for the sub zones according to the coverage situations of the sub zones; an input instruction is determined according to the coding of each sub zone and the corresponding element values of the sub zones. With the adoption of the instruction identification method, the input instruction can be obtained through accurate and efficient hand gesture recognition, and the input efficiency is further improved.

Description

technical field [0001] The present invention relates to the technical field of gesture recognition, in particular to an instruction recognition method and electronic equipment. Background technique [0002] Gesture recognition has a wide range of applications in the technical field of human-computer interaction. Current gesture recognition is usually vision-based gesture recognition, which utilizes one or more cameras and computer vision techniques to interpret gestures. [0003] In the process of human-computer interaction based on vision-based gesture recognition, multiple steps such as gesture segmentation, gesture recognition and gesture analysis are required. Among them, gesture segmentation needs to segment gesture regions in images with complex backgrounds. During the gesture interaction process, when objects with similar skin colors appear in the background, segmentation errors are prone to occur. For example, non-gesture regions Mistaking a gesture area or mistaki...

AI Technical Summary

Problems solved by technology

In addition, in the process of gesture recognition, it is necessary for the classifier to quickly compare the gestures to be recognized with the trained sample gestures and reduce classification errors. However, since the gesture feature library is usually large and requires a lot of calculations, the gesture recognition time Longer, which affects the efficiency of gesture recognition

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