| Title: | Research on vehicle re-identification algorithm based on fusion attention method |
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| Authors: | ID Chen, Peng (Author)
ID Liu, Shuang (Author)
ID Kolmanič, Simon (Author) |
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| Files: |  Chen-2023-Research_on_Vehicle_Re-Identificatio.pdf (5,28 MB)
MD5: F5C6574D28B30876DD1E5FCEBF393F11
 https://www.mdpi.com/2076-3417/13/7/4107
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | 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. |
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| Keywords: | vehicle re-identification, attention mechanism, key-point, local feature, feature fusion |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 03.02.2023 |
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| Article acceptance date: | 22.03.2023 |
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| Publication date: | 23.03.2023 |
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| Publisher: | MDPI |
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| Year of publishing: | 2023 |
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| Number of pages: | Str. 17 |
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| Numbering: | Letn. 13, št. 7, št. članka 4107 |
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| PID: | 20.500.12556/DKUM-87006-cb3c8055-01b7-9e4c-1564-f8810270c0da  |
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| UDC: | 004.9 |
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| ISSN on article: | 2076-3417 |
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| COBISS.SI-ID: | 162907139 |
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| DOI: | 10.3390/app13074107 |
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| Publication date in DKUM: | 06.02.2024 |
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| Views: | 481 |
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| Downloads: | 49 |
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| Metadata: |    |
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| Categories: | Misc.
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