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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2022.3202370
|2 doi
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|a pubmed24n1153.xml
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|a DE-627
|b ger
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Li, Hongchao
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Attribute and State Guided Structural Embedding Network for Vehicle Re-Identification
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 16.09.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Vehicle re-identification (Re-ID) is a crucial task in smart city and intelligent transportation, aiming to match vehicle images across non-overlapping surveillance camera scenarios. However, the images of different vehicles may have small visual discrepancies when they have the same/similar attributes, e.g., the same/similar color, type, and manufacturer. Meanwhile, the images from a vehicle may have large visual discrepancies with different states, e.g., different camera views, vehicle viewpoints, and capture time. In this paper, we propose an attribute and state guided structural embedding network (ASSEN) to achieve discriminative feature learning by attribute-based enhancement and state-based weakening for vehicle Re-ID. First, we propose an attribute-based enhancement and expanding module to enhance the discrimination of vehicle features through identity-related attribute information, and we design an attribute-based expanding loss to increase the feature gap between different vehicles. Second, we design a state-based weakening and shrinking module, which not only weakens the state information that interferes with identification but also reduces the intra-class feature gap by a state-based shrinking loss. Third, we propose a global structural embedding module that exploits the attribute information and state information to explore hierarchical relationships between vehicle features, then we use these relationships for feature embedding to learn more robust vehicle features. Extensive experiments on benchmark datasets VeRi-776, VehicleID, and VERI-Wild demonstrate the superior performance and generalization of the proposed method against state-of-the-art vehicle Re-ID methods. The code is available at https://github.com/ttaalle/fast_assen
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|a Journal Article
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| 700 |
1 |
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|a Li, Chenglong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zheng, Aihua
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tang, Jin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Luo, Bin
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 31(2022) vom: 09., Seite 5949-5962
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|x 1941-0042
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| 773 |
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|g volume:31
|g year:2022
|g day:09
|g pages:5949-5962
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2022.3202370
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