中圖分類號(hào)：TP391.4
文獻(xiàn)標(biāo)識(shí)碼：A
DOI： 10.19358/j.issn.2096-5133.2020.01.012
引用格式：張健，張永輝，何京璇。基于DenseNet和深度運(yùn)動(dòng)圖的行為識(shí)別算法［J］。信息技術(shù)與網(wǎng)絡(luò)安全，2020,39（1）：63-69.

Action recognition algorithm based on DenseNet and depth motion map

Abstract： This paper proposes a human behavior recognition algorithm based on DenseNet and DMM,which integrates depth information and rich texture information in RGB video sequence.Based on the DenseNet network structure,the algorithm firstly obtains color texture information and optical flow information,and then obtains depth information from synchronous depth video sequence to enhance feature complementarity.Three kinds of characteristic information are used as the input of spatial flow network,temporal flow network and deep flow network.Then LSTMs is used for feature fusion and behavior classification.Experimental results show that the recognition rate of UTDMHAD data set is 92.11%,which is an excellent performance compared with similar algorithms in this field.

Key words : action recognition;depth motion maps;DenseNet;optical flow