Gesture recognition method based on deep learning

Abstract

The invention discloses a gesture recognition method based on deep learning. The gesture recognition method comprises the following steps: carrying out noise reduction processing to a collected gesture image, and eliminating a color offset phenomenon in the gesture image; locking an area, where a gesture is positioned, in the image by adopting a frame difference method and a color characteristic detection method, and tracking a gesture by adopting a CamShift algorithm to obtain a gesture target; carrying out the deep learning on a gesture target image; and inputting the obtained gesture image to be recognized into a trained deep belief network model to finish the recognition and the classification of the gesture.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a gesture recognition method based on deep learning. Background technique [0002] Gesture is a natural, intuitive and concise way of human-computer interaction. Gesture recognition is based on the video images captured by the computer, using image processing, pattern recognition and other technologies to recognize and understand specific gestures and their meanings in the image, and complete the operation and control of computers and household appliances. Gesture recognition technology has a wide range of applications in human-computer interaction, mobile terminals, information appliances, entertainment games and other fields. In the usual gesture recognition system, before recognition and classification, gesture feature extraction is required. Since the gesture feature extraction must satisfy the characteristics of rotation, translation and scale invariance, t...

AI Technical Summary

Problems solved by technology

In the usual gesture recognition system, before recognition and classification, gesture feature extraction is required. Since the gesture feature extraction must satisfy the characteristics of rotation, translation and scale invariance, the selected features are very limited, which also limits The recognition accuracy of the gesture recognition system

At the same time, due to the classifiers used in traditional gesture recognition, such as support vector machine (SVM), Boosting, Logistic regression, etc., the structure of these models can basically be regarded as containing only one hidden layer, or there is no hidden layer. These models It belongs to the shallow learning model, which has limited learning ability and cognitive ability to data.

[0003] Dong Lifeng proposed in the document "Static Gesture Recognition and Application Based on Hu Moment and Support Vector Machine" that the Hu moment is selected as the feature of the gesture to be recognized. The Hu moment has the characteristic that it does not change with the image rotation, translation and scale change; In the recognition stage, the support vector machine is used to classify gestures, and 10 different static gestures are recognized, and the recognition accuracy rate can reach 93%. However, this method has the following defects: 1. It is necessary to extract gesture features as the input of the classifier, There are great limitations when selecting features; 2. The selected features are relatively single, which affects the gesture classification and recognition effect; 3. The support vector machine belongs to the shallow learning machine. Compared with the deep classifier of deep learning, its The classification effect is relatively poor; 4. For 10 different gestures, the recognition rate is not high and needs to be improved

This method has the following defects: 1. Use a special video input device to obtain the gesture image and its depth information. This kind of equipment is relatively expensive and the cost is high; high, time-consuming

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