Action recognition method

Abstract

The present invention provides an action recognition method, including: segmenting an action video sample, and calculating a dynamic image of each video segment; inputting the dynamic image and a static image of each video segment into a feature extractor; extracting a motion feature vector of the dynamic image; constructing a feature center group to acquire a histogram expression; inputting the histogram expressions into a histogram connecting layer to acquire a complete histogram expression of the action video sample; then, inputting the complete histogram expression into a multilayer perceptron to form a motion feature quantization network; training the motion feature quantization network to converge; inputting the dynamic and static images into a feature extractor, a feature enhancer and a feature soft quantizer in the trained motion feature quantization network to acquire a histogram expression; inputting the histogram expression into a salient motion feature extractor to acquire a salient motion feature map; inputting the salient motion feature map into a convolutional neural network to form an action classifier; training the action classifier to converge; calculating dynamic and static images of each segment of a test action video sample, and inputting the calculated dynamic and static images into the trained action classifier to implement action recognition.