摘要
针对车辆型号相同但车辆个体不同的重识别问题,提出一种新的车辆重识别算法。运用部件检测算法获取不同车辆之间差异较大的车窗和车脸区域,对检测到的车窗和车脸区域进行特征提取并进行融合,生成新的融合特征,计算图像特征之间距离度量进行分类识别。在中山大学公开数据集VRID-1上进行测试,结果表明,该算法的Rank1匹配率达到66.67%,明显优于经典的传统特征表征算法,从而验证该算法是可行且有效的。
To address the re-identification problem of different individual vehicles with identical types,a new vehicle re-identification algorithm is proposed.According to the component detection algorithm,the window and the vehicle face region with large differences between different vehicles are obtained,and the vehicle features of the detected vehicle window and the vehicle face region are extracted and merged to generate new fusion features.The distance measurement between image features is calculated for classification and recognition.The test is carried out on the public dataset VRID-1 of Sun Yat-sen university and results show that the Rank1 matching rate of the algorithm reaches 66.67%,which is obviously better than the classical traditional feature representation algorithm,thus verifies the feasibility and validity of the algorithm.
作者
李熙莹
周智豪
邱铭凯
LI Xiying;ZHOU Zhihao;QIU Mingkai(School of Intelligent Systems Engineering,Sun Yat-sen University,Guangzhou 510006,China;Guangdong Province Key Laboratory of Intelligent Transportation System,Guangzhou 510006,China;Key Laboratory of Video and Image Intelligent Analysis and Application Technology, Ministry of Public Security of PRC,Guangzhou 510006,China;National Engineering Laboratory of Video and Image Information Intelligent Analysis and Sharing Application Technology, Beijing 100048,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2019年第6期12-20,共9页
Computer Engineering
基金
国家自然科学基金“视频大数据高效表达、深度分析与综合利用”(U1611461)
关键词
车辆重识别
部件检测
特征提取
特征融合
距离度量
vehicle re-identification
component detection
feature extraction
feature fusion
distance measurement
