Action recognition algorithm based on DenseNet and depth motion map
Zhang Jian,Zhang Yonghui,He Jingxuan
（Hainan University,Haikou 570228,China）
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 UTDMHAD 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