作者:李晓楠，朱朦，任洪娥，陶锐
Authors:LI Xiao nan,ZHU Meng,REN Hong e,TAO Rui摘要:为解决东北虎重识别研究中存在的细节特征提取不充分等问题 ,提出了一种融合多分支与多粒度特征的东北虎重识别模型 CMM-Net。其中 ,全局分支负责提取宏观上的粗粒度特征 ;注意力分支通过插入坐标注意力模块加深了网络对重要特征的关注度 ;局部分支通过将特征图切分成不同条带块 ,从而提取东北虎更细粒度的局部特征 。通过多个分支结构和多个细粒度特征结合来对模型进行优化学习 ,加强全局特征与局部特征的关联性 。同时提出用 Circle Loss 与 Softmax 的联合损失来提高网络精度 。实验结果表明 ,在ATRW 数据集上所提模型在单摄像头环境下mAP 为93. 6% ,跨摄像头环境下mAP 为 77. 4% ,均优于多数文献所提方法 ,证明了本文模型的 有效性。
Abstract: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.

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