国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Aiming at the problem that the original C3D convolutional neural network has a small number of layers, a large amount of parameters, and the difficulty of focusing on key frames lead to the low accuracy of human behavior recognition, an improved C3D-based attention residual network model is proposed. First, add the original network convolution layer and use the convolution kernel merge and split operation to realize the asymmetric convolution kernel of (3x1x7)and(3x7x1), and then the fully pre-activated residual network structure is used to increase the constructed asymmetric convolutional layer, and the spatiotemporal channel attention module is added to the residual block. Finally, in order to demonstrate the advancement and applicability of the algorithm, the algorithm is compared with the original C3D network and other popular algorithms on the benchmark data set HMDB51 and the self-built 43 categories sports data set. Experimental results show that compared with the original C3D network, the algorithm has increased by 9.88% and 21.61% on the HMDB51 and 43 types of sports data sets, respectively, and the amount of parameters has been reduced by 38.68%, and the results are better than other popular algorithms.