A Human-Computer Interaction Method Based on Artificial Intelligence

Abstract

The invention discloses a human-computer interaction method based on artificial intelligence, comprising the following steps: establishing a recognition model; inputting a video into the recognition model, recognizing the video, and obtaining dynamic gestures of characters in the video; the recognition model includes a spatial channel sub-model And the temporal channel sub-model, the spatial channel sub-model processes the spatial information for video frames, and the temporal channel sub-model processes the information for the timing information and motion features of the video clips. The artificial intelligence-based human-computer interaction method disclosed by the invention has many advantages, such as high recognition accuracy, high frame rate, high speed, and the like.

Description

technical field [0001] The invention relates to a human-computer interaction method based on artificial intelligence, in particular to a dynamic conference gesture recognition method, which belongs to the technical field of image recognition and detection. Background technique [0002] In computer vision recognition, we can classify images and detect objects in images. At present, models based on convolutional neural networks have achieved image cognition tasks. However, such models have serious limitations in understanding image sequences, and cannot identify the semantic correlation between consecutive images, that is, they cannot understand dynamic behaviors. to identify or understand. [0003] However, in the real world, most behaviors cannot be judged by static pictures. For example, when a picture is extracted from the middle of a zoom-out or zoom-in gesture, the static pictures are basically the same, even for humans, it is difficult to distinguish. [0004] Althoug...

AI Technical Summary

Problems solved by technology

At present, the model based on convolutional neural network has achieved the task of image cognition. However, this type of model has serious limitations in understanding image sequences, and cannot identify the semantic correlation between consecutive images, that is, it cannot respond to dynamic behaviors. to recognize or understand

[0003] But in the real world, most behaviors cannot be judged by static pictures. For example, a picture is extracted from the middle process of zooming out or zooming in. The static pictures are basically the same, even for humans, it is difficult to distinguish

[0004] Although there are dynamic gesture recognition products or methods such as Kinect, they all need specific hardware equipment, so they do not have universality; in addition, such products or methods have higher requirements for users, and the operation before use complicated steps

[0005] Moreover, the traditional recognition method has low accuracy and stability for dynamic gesture recognition, slow recognition speed, and poor user experience

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