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Overview: People re-identification is mainly used to retrieve pedestrians of interest in the images taken by the camera, and then retrieve targets similar to the people's image. This technology can save a lot of time and manpower in finding the images of the suspect in the pedestrian database, and has good application prospects in intelligent security, criminal investigation, and image retrieval. The supervised person re-identification model has better recognition accuracy, but there are scalability problems. For example, the accuracy of algorithm identification relies heavily on effective supervised information. When adding a small amount of data in the classification process, all data needs to be reprocessed, resulting in poor real-time performance. Aiming at the above problems, an unsupervised person re-identification algorithm based on soft multilabel is proposed. By learning the feature of the target, and then comparing it with the labeled reference datasets, each unlabeled target gets a soft multilabel. In this learning process, in order to obtain more accurate soft multilabel, we introduce the concept of reference agents and in order to reduce the difference between reference agents and labeled reference datasets, we pre-trained the reference datasets. Using a reference agent instead of a labeled reference dataset to compare with an unlabeled target. We also use three loss functions, which are used to mine hard negative pair information, make the cross-camera labels of the same target consistent, and correct cross-domain distribution misalignment. In these three loss functions, the purpose of mining hard negative pair information is to determine negative pairs more accurately and push the distance of negative pairs farther away; The cross-camera label consistency is to reduce the gap between multilabel for the same target under different camera distributions. Using the simplified 2-Wasserstein distance, the mean and standard deviation vectors of soft multilabel in different camera views are calculated; In order to further improve the effectiveness of the reference agent and solve the problem of cross-domain distribution misalignment, for each reference agent, find unlabeled people close to it and design a loss function. In the process of feature extraction, we use multi-level deep feature fusion to complement deep features with shallow features to achieve the purpose of improving feature robustness and thereby improving the recognition accuracy. We also tried to integrate squeeze-and-excitation networks (SENet) into the residual network to achieve a function similar to the attention mechanism to improve the learning speed. Experimental results show that rank-1 and mAP in this paper are superior to advanced correlation algorithms.
Soft multilabel learning loss function illustrate
ResNet-50 illustrate
SE_ResNet illustrate
Experimental model illustrate
Results of adjusting hyperparameters for SE_ResNet-50. (a) Adjusting the experimental results of the epoch parameters; (b) Adjusting the learning rate experimental results; (c) Adjusting the weight decay experimental results