Gesture image classification method and device, computer equipment and storage medium

Abstract

The embodiment of the invention provides a gesture image classification method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the detection of aplurality of inputted gesture images, so as to obtain the number of fingertips in each gesture image, and dividing the plurality of gesture images into a plurality of groups according to the detectednumber of fingertips; performing classification training on the gesture images of each group to obtain classifier functions of each group; and respectively calculating the gesture images of each groupby utilizing the classifier function of each group so as to obtain a gesture type classification result of the gesture images of each group. According to the embodiment of the invention, the gestureimage is divided into a plurality of groups by detecting the number of fingertips of the gesture image; preliminary classification of the gesture images is achieved, gesture type classification of thegesture images is achieved based on the preliminary classification result, and compared with an existing gesture image classification algorithm, the operand and and complexity of the algorithm are greatly reduced, and the algorithm processing speed is increased.

Description

technical field [0001] The present disclosure relates to the field of communication technologies, and in particular, to a gesture image classification method, a gesture image classification device, a computer device, and a computer-readable storage medium. Background technique [0002] Gesture image classification has many application scenarios, such as games, IPTV (Internet Protocol Television, Internet Protocol Television) control, and the like. There are generally two indicators to evaluate the quality of gesture image classification algorithms: one is the accuracy of gesture recognition, and the other is the real-time performance of gesture classification algorithms. Especially for interactive game scenarios, it is necessary to improve the operating efficiency of the algorithm while ensuring accuracy. [0003] In related technologies, gesture image classification algorithms generally use Hu moment (geometric invariant moment) features for gesture recognition, and obtain...

AI Technical Summary

Problems solved by technology

However, as the types of gestures increase, simply using the Hu moment classifier for gesture image classification will increase the complexity of the algorithm sharply, and the algorithm delay will be greatly increased

In general, optimized classifier algorithms (such as BP (back propagation, backpropagation) neural network algorithm, hidden Markov algorithm) are used in engineering to optimize the complexity of the algorithm, but it still cannot reduce the classification to a great extent. Algorithm calculation

It can be seen that when there are many types of gestures, no matter what classification algorithm is used to obtain the classifier, the complexity of gesture classification will increase greatly, resulting in the classification results not being returned in real time

Images