In order to solve the problem of insufficient detailed feature extraction in the re-identification of the Amur tiger

a re- identification model of the Amur tiger

CMM-NET

was proposed

which combined multi-branch and multi-granularity features. The global branch is responsible for extracting macroscopic coarse-grained features. The attention branch deepens the network ′s attention to important features by inserting coordinate attention module. Local branches can extract finer grained local features of amur tigers by cutting the feature map into different blocks. Finally

the model is optimized by combining multiple branch structures and multiple fine- grained features to strengthen the correlation between global features and local features. Meanwhile

the combined Loss of Circle Loss and Softmax is proposed to improve the network accuracy. Experimental results show that the mAP of the model proposed on THE ATRW data set is 93. 6% in a single camera environment and 77. 4% in a cross-camera environment

both of which are better than the methods proposed in most literatures

proving the effectiveness of the proposed model.