基于多元信息融合的车路协同重识别算法

doi:10.3901/JME.2024.16.231

机械工程学报 ›› 2024, Vol. 60 ›› Issue (16): 231-240.doi: 10.3901/JME.2024.16.231

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基于多元信息融合的车路协同重识别算法

钱敏, 耿可可, 殷国栋, 李尚杰, 王子威, 孙宇啸

东南大学机械工程学院南京 211189

收稿日期:2023-10-11 修回日期:2024-04-03 出版日期:2024-08-20 发布日期:2024-10-21
作者简介:钱敏,男,1998年出生。主要研究方向为车路协同感知技术及其应用。E-mail:qianmin@seu.edu.cn
殷国栋(通信作者),男,1976年出生,博士,教授,博士研究生导师。主要研究方向为先进电动汽车、车辆动力学控制、智能无人汽车和车辆主动安全控制。E-mail:ygd@seu.edu.cn
基金资助:
国家自然青年基金(51905095)、江苏省重点研发计划(BE2019004)、国家杰出青年科学基金(52025121)和国家自然科学基金(51975118)资助项目。

Re-identification Algorithm for Cooperative Vehicle-infrastructure System Based on Multi-information Fusion

QIAN Min, GENG Keke, YIN Guodong, LI Shangjie, WANG Ziwei, SUN Yuxiao

School of Mechanical Engineering, Southeast University, Nanjing 211189

Received:2023-10-11 Revised:2024-04-03 Online:2024-08-20 Published:2024-10-21

摘要/Abstract

摘要： 针对车路协同系统的感知信息冗余问题，提出一种基于多元信息融合的重识别算法。算法输入为两个目标的图像与轨迹，通过计算图像相似度与轨迹相似度，并设计考虑多种权重的融合策略，最终得到两个目标的相似度，并做出对应的重识别处理。对于图像相似度，设计一种轻量化的图像相似度孪生网络；对于轨迹相似度，设计一种轨迹相似度算法；对于融合策略，通过计算图像质量权重、轨迹质量权重、图像相似度权重、轨迹相似度权重等四种权重，使用融合函数实现对图像与轨迹信息的融合。搭建真实交通场景下的车路协同系统，并基于该系统进行多种工况的试验来验证算法的有效性。结果表明，相较于SVDNet、Cam-GAN、MultiScale等基于单一信息元的重识别算法，基于多元信息融合的车路协同重识别算法提高了30%以上的重识别正确率和降低了20%以上的重识别漏检率。

关键词: 车路协同, 重识别, 图像相似度, 轨迹相似度, 多元信息融合

Abstract: A re-identification algorithm based on multi-information fusion is proposed to solve the problem of perception information redundancy in cooperative vehicle-infrastructure system(CVIS). The inputs of algorithm are the images and trajectories of the two observed targets. By calculating the image similarity and the trajectory similarity, and designing a fusion strategy considering multiple weights, the overall similarity of the two targets can be obtained. Then, a re-identification process is made according to overall similarity of the two targets. For the image similarity, a lightweight image similarity Siamese network is designed; for the trajectory similarity, a trajectory similarity algorithm is designed; for the fusion strategy, four kinds of weights, including image quality weight, trajectory quality weight, image similarity weight, and trajectory similarity weight, are calculated. A fusion function is proposed to realize the fusion of image and trajectory information. A platform of CVIS is built in real traffic scenario. Based on this platform, a variety of experiments under different working conditions are carried out to verify the effectiveness of the algorithm. The results show that compared to the re-identification algorithms based on single information source such as SVDNet, Cam-GAN, MultiScale, the re-identification algorithm based on multi-information fusion improves the re-identification accuracy rate by more than 30% and reduce missed detection rate by more than 20%.

Key words: cooperative vehicle-infrastructure system, re-identification, image similarity, trajectory similarity, multi-information fusion

中图分类号:

U461

钱敏, 耿可可, 殷国栋, 李尚杰, 王子威, 孙宇啸. 基于多元信息融合的车路协同重识别算法[J]. 机械工程学报, 2024, 60(16): 231-240.

QIAN Min, GENG Keke, YIN Guodong, LI Shangjie, WANG Ziwei, SUN Yuxiao. Re-identification Algorithm for Cooperative Vehicle-infrastructure System Based on Multi-information Fusion[J]. Journal of Mechanical Engineering, 2024, 60(16): 231-240.

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基于多元信息融合的车路协同重识别算法

Re-identification Algorithm for Cooperative Vehicle-infrastructure System Based on Multi-information Fusion

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