Xue L X, Zhu Z F, Wang R G, et al. Person re-identification by multi-division attention[J]. Opto-Electron Eng, 2020, 47(11): 190628. doi: 10.12086/oee.2020.190628
Citation: Xue L X, Zhu Z F, Wang R G, et al. Person re-identification by multi-division attention[J]. Opto-Electron Eng, 2020, 47(11): 190628. doi: 10.12086/oee.2020.190628

Person re-identification by multi-division attention

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  • Person re-identification is significant but a challenging task in the computer visual retrieval, which has a wide range of application prospects. Background clutters, arbitrary human pose, and uncontrollable camera angle will greatly hinder person re-identification research. In order to extract more discerning person features, a network architecture based on multi-division attention is proposed in this paper. The network can learn the robust and discriminative person feature representation from the global image and different local images simultaneously, which can effectively improve the recognition of person re-identification tasks. In addition, a novel dual local attention network is designed in the local branch, which is composed of spatial attention and channel attention and can optimize the extraction of local features. Experimental results show that the mean average precision of the network on the Market-1501, DukeMTMC-reID, and CUHK03 datasets reaches 82.94%, 72.17%, and 71.76%, respectively.
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  • Overview:With the popularity of surveillance cameras in public areas, person re-identification has become more and more important, and has become a core technology in video content retrieval, video surveillance, and intelligent security. However, in actual application scenarios, due to factors such as camera shooting angle, complex lighting changes, and changing pedestrian poses, occlusions, clothes, and background clutter in person images. It makes even the same person target have significant differences in different cameras, which poses a great challenge for person re-identification research. Therefore, in this paper we propose a research method based on deep convolutional networks, which combines global and local person feature and attention mechanisms to solve the problem of person re-identification. First, unlike traditional methods, we use ResNet50 network to initially extract person image features with more discriminating ability. Then, according to the person inherent body structure, the image is divided into several bands in the horizontal direction, and it is input into the local branch of the built-in attention mechanism to extract the person local attention features. At the same time, the global image is input to the global branch to extract the person global features. Finally, the person global features and local attention features are fused to calculate the loss function. In the network, in order to better extract the person local features, we design two local branches to segment the person images into different numbers of local area images. With the increase of the number of blocks, the network will learn more detailed and discriminative local features in each different local area, and at the same time, it can filter irrelevant information in local images to a large extent by combining the attention mechanism. Our proposed attention mechanism can make the network focus on the areas that need to be identified. The output person attention features usually have a stronger response than the non-target areas. Therefore, the attention networks we design include spatial attention networks and channel attention networks, which complement each other to learn the optimal attention feature, thereby extracting more discriminative local features. Experimental results show that the method proposed in this paper can effectively improve the performance of person re-identification.

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