Research on Vehicle Re-Identification Algorithm Based on Fusion Attention Method
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Video Surveillance and Tracking Methods · Advanced Neural Network Applications · Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Software, Dalian Neusoft University of Information, Dalian 116023, China
School of Computer Science and Engineering, Dalian Minzu University, Dalian 116600, China
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroska cesta 46, 2000 Maribor, Slovenia
Funding
Chinese-Slovenian Scientific and Technological 2021 Cooperation Project of the Ministry of Science and Technology · Liaoning Province Economic and Social Development Research Project 2023 of Provincial Social Science Association
References (32)
Show source-provided references
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