Gesture recognition method based on depth image and gesture recognition system based on depth images

Abstract

The invention discloses a gesture recognition method based on depth images. The method comprises the following steps: acquiring depth images in a training data set and a test date set by virtue of a depth sensor; calculating the minimal depth values of human body regions in the images, and partitioning gestures in the depth images by use of a depth threshold in combination with a preset condition that a human hand is an object nearest to the sensor; then acquiring projection drawings of the gesture on three orthogonal planes, namely a front-view projection drawing, a side-view projection drawing and a top-view projection drawing; further extracting the outline fragment packet characteristics of the three projection drawings and cascading to form a characteristic vector of the original depth gesture; and finally classifying the gesture characteristic vectors acquired from the depth images to be recognized by virtue of a training classifier, thereby obtaining recognition results of the to-be-recognized gestures. The invention further provides a corresponding gesture recognition system. The method is simple and practical in gesture recognition, high in popularization capability and high in recognition accuracy and can be used for effectively overcoming the influence of adverse factors such as busy background, illumination, noises and self occlusion.

Description

Technical field [0001] The present invention belongs to the field of computer vision technology, and more specifically, relates to a gesture recognition method and system based on depth images. Background technique [0002] Gesture recognition has received attention due to its wide application in virtual reality, sign language recognition and human-computer interaction (HCI, computer games). Despite a lot of preliminary work, the application of traditional vision-based gesture recognition methods in real life is still far from satisfactory. Due to the nature of optical sensing, the optical sensor-based method is sensitive to light conditions and cluttered background. Therefore, it is generally unable to robustly detect and track hands, which greatly affects the performance of gesture recognition. In order to provide more robust gesture recognition, one of the effective ways is to use other sensors to capture gestures and movements, such as through data gloves. Unlike optical se...

AI Technical Summary

Problems solved by technology

However, it is very difficult to efficiently obtain local support surfaces

In addition, the classification accuracy of the H3DF-based gesture recognition method on complex large gesture datasets needs to be further improved

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