Local Feature-Aware Siamese Matching Model for Vehicle Re-Identification
Source abstract
Abstract
Vehicle re-identification is attracting an increasing amount of attention in intelligent transportation and is widely used in public security. In comparison to person re-identification, vehicle re-identification is more challenging because vehicles with different IDs are generated by a unified pipeline and cannot only be distinguished based on the subtle differences in their features such as lights, ornaments, and decorations. In this paper, we propose a local feature-aware Siamese matching model for vehicle re-identification. A local feature-aware Siamese matching model focuses on the informative parts in an image and these are the parts most likely to differ among vehicles with different IDs. In addition, we utilize Siamese feature matching to better supervise our attention. Furthermore, a perspective transformer network, which can eliminate image deformation, has been designed for feature extraction. We have conducted extensive experiments on three large-scale vehicle re-ID datasets, i.e., VeRi-776, VehicleID, and PKU-VD, and the results show that our method is superior to the state-of-the-art methods.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Video Surveillance and Tracking Methods · Vehicle License Plate Recognition · Advanced Neural Network Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer Science and Technology, Zhejiang University, No. 38 Zheda Road, Hangzhou 310027, China
Institute of Advanced Digital Technology and Instrument, Zhejiang University, No. 38 Zheda Road, Hangzhou 310027, China
Alibaba Group, Ali Yun Feitian Park, Zhuan Tang Street, West Lake District, Hangzhou 310000, China
Funding
the National Key R&D program of China
References (43)
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