北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (9): 1650-1659.doi: 10.13700/j.bh.1001-5965.2020.0067

• 论文 • 上一篇    下一篇

基于高置信局部特征的车辆重识别优化算法

窦鑫泽1,2, 盛浩1,2, 吕凯1,2, 刘洋1,2, 张洋1,2, 吴玉彬1,2, 柯韦3   

  1. 1. 北京航空航天大学 计算机学院 软件开发环境国家重点实验室, 北京 100083;
    2. 北京航空航天大学 大数据与脑机智能高精尖创新中心, 北京 100083;
    3. 澳门理工学院 应用科学高等学校, 澳门 999078
  • 收稿日期:2020-03-02 发布日期:2020-09-22
  • 通讯作者: 盛浩 E-mail:shenghao@buaa.edu.cn
  • 作者简介:窦鑫泽 男,硕士研究生。主要研究方向:计算机视觉;盛浩 男,博士,副教授,博士生导师。主要研究方向:计算机视觉、模式识别和机器学习;吕凯 男,博士研究生。主要研究方向:计算机视觉;刘洋 男,博士研究生。主要研究方向:计算机视觉;张洋 男,博士研究生。主要研究方向:计算机视觉;吴玉彬 男,博士研究生。主要研究方向:计算机视觉;柯韦 男,博士,副教授。主要研究方向:模式识别。
  • 基金资助:
    国家重点研发计划(2018YFB2100500);国家自然科学基金(61861166002,61872025,61635002);澳门特别行政区科学技术发展基金(0001/2018/AFJ);软件开发环境国家重点实验室开放基金(SKLSDE2019ZX-04)

Vehicle re-identification optimization algorithm based on high-confidence local features

DOU Xinze1,2, SHENG Hao1,2, LYU Kai1,2, LIU Yang1,2, ZHANG Yang1,2, WU Yubin1,2, KE Wei3   

  1. 1. State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, Beijing 100083, China;
    2. Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100083, China;
    3. School of Applied Sciences, Macao Polytechnic Institute, Macao SAR 999078, China
  • Received:2020-03-02 Published:2020-09-22
  • Supported by:
    National Key R & D Program of China (2018YFB2100500); National Natural Science Foundation of China (61861166002, 61872025, 61635002); Science and Technology Development Fund, Macau SAR (0001/2018/AFJ); Open Fund of the State Key Laboratory of Software Development Environment (SKLSDE2019ZX-04)

摘要: 根据车辆重识别中区域置信度不同,提出了基于高置信局部特征的车辆重识别优化算法。首先,利用车辆关键点检测获得对应的多个关键点坐标信息,分割出车标扩散区域和其他重要的局部区域。根据车标扩散区域的高区分度特性,提升局部区域的置信度。使用多层卷积神经网络对输入图片进行处理,根据局部区域分割信息,对卷积得到的特征张量进行空间维度上的切割,获得代表全局信息和关键局部信息的特征张量。然后,通过全连接层特征张量转化为表示车辆个体的一维向量,计算损失函数。最后,在测试阶段使用全局特征,并利用训练好的车标扩散区域提取分支获得高置信局部特征,缩短局部识别一致的车辆目标距离。在典型车辆重识别数据集VehicleID上进行测试,验证了所提算法的有效性。

关键词: 车辆重识别, 高置信局部特征, 特征优化, 区域检测, 神经网络

Abstract: In solving vehicle re-identification problems, different vehicle regions have different recognition degree of confidence. Based on this observation, we propose a vehicle re-identification optimization algorithm that takes advantage of the high-confidence local features. First, the vehicle key point detection algorithm is utilized to obtain the corresponding multiple key points' coordinate information of the vehicles, and to divide the vehicle brand extension regions and other prominent local regions. As the brand extension region is the most salient region, we propose to improve the degree of confidence of the local region in the testing phase. We also utilize a multi-layer convolutional neural network for processing the input images, cutting the convolutional features into several parts based on the obtained local regions, and acquiring feature tensors representing global and key regional information. Then, a fully connected layer is applied to combine the above features and output a one-dimensional vector for loss function calculating. In the testing phase, to reduce the target distances of vehicles with the same local identification, we propose to utilize the global features together with the high-confidence local features obtained by trained brand extension region extraction branch. Experiments on the widely used vehicle re-identification VehicleID dataset show that the proposed algorithm is effective.

Key words: vehicle re-identification, high-confidence local features, feature optimization, region detection, neural network

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