Abstract
Image re-identification is usually used to find specific images from image libraries or video sequences. In recent years, convolutional neural networks have gradually become the dominant method in this field. In this paper, a Multitask Identification-Verification Siamese Network for vehicles is proposed, which combines color feature vectors with the inherent structure, category and other attributes of the vehicle. A vehicle 2D structural model and a method of image viewpoint normalization are also presented to ensure the high consistency of features in the expression of images from different angles. In addition, based on the vehicle 2D structural model, a dynamic annular non-uniform partition color super-pixel sampling strategy for vehicle face is investigated to construct a color feature vector. In the experiments, the proposed model and method are evaluated on the public vehicles and VeRi datasets. The experimental results show that the proposed method and model have made great progress.
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Funding
This study was funded by the Zhejiang Provincial Science and Technology Planning Key Project of China (No. 2018C01064) and Natural Science Foundation of Zhejiang Province (CN) (No. LY19F020027).
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Gao, F., Shen, X., Ge, Y. et al. MT-IVSN: a novel model for vehicle re-identification. J Ambient Intell Human Comput 13, 3565–3576 (2022). https://doi.org/10.1007/s12652-020-01988-y
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DOI: https://doi.org/10.1007/s12652-020-01988-y