基于深度学习的车辆重识别研究进展

doi:10.3778/j.issn.1002-8331.2409-0384

摘要/Abstract

摘要： 随着智能监控和公共安全领域对车辆重识别技术需求日益增长，基于深度学习的方法凭借强大的图像处理能力逐渐成为研究的热点。传统的手工特征方法已无法满足现代车辆重识别面临的海量数据处理需求。梳理了当前基于深度学习的车辆重识别研究。介绍了车辆重识别的背景知识。根据数据输入源的不同，将现有方法分为表征学习和跨域学习两大类。表征学习关注全局特征和辅助特征的提取与融合，跨域学习则致力于处理不同领域之间的适应性问题。系统地总结了各类方法的关键技术，评述了它们的优势与局限性。最后探讨了未来研究的方向，提出通过多模态数据融合、无监督学习方法、大语言模型等先进技术来进一步提升车辆重识别的准确性和鲁棒性。

关键词: 深度学习, 车辆重识别, 表征学习, 特征提取, 生成模型

Abstract: With the growing demand for vehicle re-identification in intelligent surveillance and public safety, deep learning-based methods have become a research hotspot due to their powerful image processing capabilities. Traditional handcrafted feature methods can no longer meet the needs of modern vehicle re-identification facing massive data processing. This paper summarizes the current research on vehicle re-identification based on deep learning. It categorizes existing methods into representation learning and cross-domain learning based on data input sources. Representation learning focuses on the extraction and fusion of global and auxiliary features, while cross-domain learning addresses adaptability issues between different domains. It reviews key technologies of various methods and discusses their advantages and limitations. Future research directions are explored, advancements in vehicle re-identification accuracy and robustness are proposed through multimodal data fusion, unsupervised learning methods, and large language models.

Key words: deep learning, vehicle re-identification, representation learning, feature extraction, generate model

平灿, 李雷孝, 刘东江, 林浩, 史建平. 基于深度学习的车辆重识别研究进展[J]. 计算机工程与应用, 2025, 61(16): 16-37.

PING Can, LI Leixiao, LIU Dongjiang, LIN Hao, SHI Jianping. Research Progress of Vehicle Re-Identification Based on Deep Learning[J]. Computer Engineering and Applications, 2025, 61(16): 16-37.

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