Human body action recognition method based on cyclic convolutional neural network

Abstract

The invention discloses a human body action recognition method based on a cyclic convolutional neural network, belongs to the field of image classification, pattern recognition and machine learning, and solves the problems of low human body action recognition precision and the like caused by changes inside action categories and between the categories or video composed of continuous frames. The method comprises: constructing a data set, namely randomly selecting sequence pairs with the same length from a public data set, and each frame in each sequence comprising an RGB image and an optical flow image; constructing a twin network, wherein each network in the twin network sequentially comprises a CNN layer, an RNN layer and a Temporal Pooling layer; constructing an 'identification-verification' joint loss function; training a constructed deep convolutional neural network and an 'identification-verification' joint loss function based on the data set; and based on the to-be-recognized human body action sequence pair, sequentially passing through the trained deep convolutional neural network and the trained 'identification-verification' joint loss function to obtain an action category recognition result of the sequence pair. The method is used for human body action recognition in the image.

Description

technical field

[0001] A human action recognition method based on a cyclic convolutional neural network is used for human action recognition in images, and belongs to the fields of image classification, pattern recognition and machine learning. Background technique

[0002] Human action recognition is one of the hotspots and cutting-edge research topics in the field of computer vision and machine learning. It has broad application prospects in intelligent video surveillance, intelligent human-computer interaction, and content-based video analysis.

[0003] The main problem to be solved in video-based human action recognition is to process and analyze the original image or image sequence data collected by the sensor (camera) through the computer, and learn and understand the human action and behavior in it. Human action recognition mainly includes the following three steps: first, detect the appearance and motion information from the image frame and extract the underlying fea...

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