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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.
Overview of our proposed MDA network for person re-identification
Detailed network of the SANet subnet
Detailed network of the CANet subnet
Top-10 ranking list for some query images
Comparison of different branch combination
Evaluations on how DLA enhances person re-identification