20.500.12556/DKUM-87006-cb3c8055-01b7-9e4c-1564-f8810270c0da Research on vehicle re-identification algorithm based on fusion attention method The specific task of vehicle re-identification is how to quickly and correctly match the same vehicle in different scenarios. In order to solve the problem of inter-class similarity and environmental interference in vehicle images in complex scenes, one fusion attention method is put forward based on the idea of obtaining the distinguishing features of details-the mechanism for the vehicle re-identification method. First, the vehicle image is preprocessed to restore the image's attributes better. Then, the processed image is sent to ResNet50 to extract the features of the second and third layers, respectively. Then, the feature fusion is carried out through the two-layer attention mechanism for a network model. This model can better focus on local detail features, and global features are constructed and named SDLAU-Reid. In the training process, a data augmentation strategy of random erasure is adopted to improve the robustness. The experimental results show that the mAP and rank-k indicators of the model on VeRi-776 and the VehicleID are better than the results of the existing vehicle re-identification algorithms, which verifies the algorithm's effectiveness. vehicle re-identification attention mechanism key-point local feature feature fusion identifikacija vozil algoritmi mehanizem pozornosti ključna točka lokalne značilnosti zlitje značilnosti true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/applsci Znanstveno delo 2024-02-06 13:34:26 2024-02-06 13:48:34 2024-11-11 14:58:34 0000-00-00 00:00:00 2023 0 0 Str. 17 št. 7, št. članka 4107 Letn. 13 March 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2023-02-03 2023-03-22 2023-03-23 004.9 162907139 10.3390/app13074107 2076-3417 Chen-2023-Research_on_Vehicle_Re-Identificatio.pdf Chen-2023-Research_on_Vehicle_Re-Identificatio.pdf 1 F5C6574D28B30876DD1E5FCEBF393F11 4ae5ecea3e995820a936932ac8ce6a678b27794e983e2c208bc4ae13b1244b9b e7371b8a-c4ed-11ee-b2aa-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=175953 https://www.mdpi.com/2076-3417/13/7/4107 0 f45359c0-c4ed-11ee-b2aa-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=175954 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0