[1] CHEN Na.Research and implementation of vehicle re-identification technology in traffic monitoring video[D].Beijing:Beijing University of Posts and Telecommuni-cations,2019.(in Chinese)陈娜.交通监控视频中车辆再识别技术研究与实现[D].北京:北京邮电大学,2019. [2] WU Mingjie,ZHANG Yongfei,ZHANG Tianyu,et al.Back-ground segmentation for vehicle re-identification[EB/OL].[2019-11-15].https://arxiv.org/pdf/1910.06613v1.pdf. [3] WANG Panpan,LI Yuhui,LI Fuwei.Vehicle re-identification based on multi-features and metric learning[J].Electronic Science and Technology,2018,31(9):29-31,79.(in Chinese)王盼盼,李玉惠,李福卫.基于特征融合和度量学习的车辆再识别[J].电子科技,2018,31(9):29-31,79. [4] BULAN O,KOZITSKY V,RAMESH P,et al.Segmentation- and annotation-free license plate recognition with deep localization and failure identification[J].IEEE Transactions on Intelligent Transportation Systems,2017,18(9):2351-2363. [5] GUO Chao,WANG Kunfeng,YAO Yanjie,et al.Vehicle license plate recognition based on extremal regions and restricted Boltzmann machines[J].IEEE Transactions on Intelligent Transportation Systems,2016,17(4):1096-1107. [6] PU Yawen,LIU Wanjun,JIANG Wentao.Identification of vehicle with block license plate based on PSO-IFCM[J].Computer Engineering,2012,38(14):157-160.(in Chinese)浦雅雯,刘万军,姜文涛.基于PSO-IFCM的遮挡车牌车辆识别[J].计算机工程,2012,38(14):157-160. [7] ZHANG Hongbing,LI Hailin,HUANG Xiaoting,et al.Research and implementation of vehicle-type recognition method based on HOG features of vehicle frontal face[J].Computer Simulation,2015,32(12):119-123.(in Chinese)张红兵,李海林,黄晓婷,等.基于车前脸HOG特征的车型识别方法研究与实现[J].计算机仿真,2015,32(12):119-123. [8] LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110. [9] SHIN H C,ROTH H R,GAO Mingchen,et al.Deep convolutional neural networks for computer-aided detection:CNN architectures,dataset characteristics and transfer learning[J].IEEE Transactions on Medical Imaging,2016,35(5):1285-1298. [10] AHMED E,JONES M,MARKS T K.An improved deep learning architecture for person re-identification[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:3908-3916. [11] ZHANG Li,XIANG Tao,GONG Shaogang.Learning a discriminative null space for person re-identification[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:1239-1248. [12] CHENG De,GONG Yihong,ZHOU Sanping,et al.Personre-identification by multi-channel parts-based CNN with improved triplet loss function[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:1335-1344. [13] XU Qian,LI Ying,WANG Gang.Pedestrian-vehicle detection based on deep learning[J].Journal of Jilin University(Engineering and Technology Edition),2019,49(5):1661-1667.(in Chinese)徐谦,李颖,王刚.基于深度学习的行人和车辆检测[J].吉林大学学报(工学版),2019,49(5):1661-1667. [14] WU F Y,YAN S Y,SMITH J S,et al.Vehicle re-identification in still images:application of semi-supervised learning and re-ranking[J].Signal Processing:Image Communication,2019,76:261-271. [15] Hangzhou Hikvision Digital Technology Co.,Ltd.Intelligent traffic network camera iDS-2CD9371-K(S)[EB/OL].[2019-11-15].https://www.hikvision.com/cn/prgs_1065_i17012.html.(in Chinese)杭州海康威视数字技术股份有限公司.智能交通网络摄像机iDS-2CD9371-K(S)[EB/OL].[2019-11-15].https://www.hikvision.com/cn/prgs_1065_i17012.html. [16] WEN Yandong,ZHANG Kaipeng,LI Zhifeng,et al.A discriminative feature learning approach for deep face recognition[C]//Proceedings of 2016 European Conference on Computer Vision.Amsterdam,the Netherlands:[s.n.],2016:499-515. [17] ZHENG L,YANG Y,HAUPTMANN A G.Person re-identification:past,present and future[EB/OL].[2019-11-15].https://arxiv.org/pdf/1610.02984.pdf. [18] YANG L J,LUO P,CHANGE L C,et al.A large scale car dataset for fine-grained categorization and verification[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:3973-3981. [19] KANACI A,ZHU X T,GONG S G.Vehicle reidentification by fine-grained cross-level deep learning[C]//Proceedings of 2017 British Machine Vision Conference.London,UK:[s.n.],2017:772-788. [20] WANG Huibing,PENG Jinjia,CHEN Dongyan,et al.Attribute-guided feature learning network for vehicle re-identification[EB/OL].[2019-11-15].https://arxiv.org/pdf/2001.03872.pdf. [21] LI Yuqi,LI Yanghao,YAN Hongfei,et al.Deep joint discriminative learning for vehicle re-identification and retrieval[C]//Proceedings of IEEE International Con-ference on Image Processing.Washington D.C.,USA:IEEE Press,2017:395-399. [22] WANG Feng,CHENG Jian,LIU Weiyang,et al.Additive margin softmax for face verification[J].IEEE Signal Processing Letters,2018,25(7):926-930. [23] CHENG Gong,YANG Ceyuan,YAO Xiwen,et al.When deep learning meets metric learning:remote sensing image scene classification via learning discriminative CNNs[J].IEEE Transactions on Geoscience and Remote Sensing,2018,56(5):2811-2821. [24] CHENG Yixian,WANG Haiyang.A modified contrastive loss method for face recognition[J].Pattern Recognition Letters,2019,125:785-790. [25] KUMA R,WEILL E,AGHDASI F,et al.Vehicle re-identification:an efficient baseline using triplet embedding[C]//Proceedings of International Joint Conference on Neural Networks.Budapest,Hungary:[s.n.],2019:1-9. [26] FAN Lin,ZHANG Jinglei.Person re-identification based on joint loss and siamese network[J].Computer Engineering and Science,2020,42(2):273-280.(in Chinese)樊琳,张惊雷.联合损失优化孪生网络的行人重识别[J].计算机工程与科学,2020,42(2):273-280. [27] DO T T,TRAN T,REID I,et al.A theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2019:10404-10413. [28] LIU Hongye,TIAN Yonghong,YANG Yaowei,et al.Deep relative distance learning:tell the difference between similar vehicles[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:2167-2175. [29] ZHANG Yiheng,LIU Dong,ZHA Zhengjun.Improving triplet-wise training of convolutional neural network for vehicle re-identification[C]//Proceedings of IEEE International Conference on Multimedia and Expo.Washington D.C.,USA:IEEE Press,2017:1386-1391. [30] BAI Yan,LOU Yihang,GAO Feng,et al.Group-sensitive triplet embedding for vehicle reidentification[J].IEEE Transactions on Multimedia,2018,20(9):2385-2399. [31] LIU Xinchen,LIU Wu,MA Huadong,et al.Large-scale vehicle re-identification in urban surveillance videos[C]//Proceedings of IEEE International Conference on Multimedia and Expo.Washington D.C.,USA:IEEE Press,2016:1-6. [32] ZHENG Liang,WANG Shengjin,ZHOU Wengang,et al.Bayes merging of multiple vocabularies for scalable image retrieval[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2014:1955-1962. [33] ZHENG Liang,SHEN Liyue,TIAN Lu,et al.Scalable person re-identification:a benchmark[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2015:1116-1124. [34] SZEGEDY C,LIU W,JIA Y Q,et al.Going deeper with convolutions[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:1-9. [35] LIU Xinchen,LIU Wu,MEI Tao,et al.A deep learning-based approach to progressive vehicle re-identification for urban surveillance[C]//Proceedings of 2016 European Conference on Computer Vision.Amsterdam,the Netherlands:[s.n.],2016:869-884. [36] TANG Yi,WU Di,JIN Zhi,et al.Multi-modal metric learning for vehicle re-identification in traffic surveillance environment[C]//Proceedings of IEEE International Conference on Image Processing.Washington D.C.,USA:IEEE Press,2017:2254-2258. [37] HEIKKILÄ M,PIETIKÄINEN M,SCHMID C.Description of interest regions with local binary patterns[J].Pattern Recognition,2009,42(3):425-436. [38] LI Xiying,ZHOU Zhihao,QIU Mingkai.Vehicle re-identification algorithm based on component fusion feature[J].Computer Engineering,2019,45(6):12-20.(in Chinese)李熙莹,周智豪,邱铭凯.基于部件融合特征的车辆再识别算法[J].计算机工程,2019,45(6):12-20. [39] LI Xiying,YUAN Minxian,JIANG Qianyin,et al.VRID-1:a basic vehicle re-identification dataset for similar vehicles[C]//Proceedings of IEEE International Conference on Intelligent Transportation Systems.Washington D.C.,USA:IEEE Press,2017:1-8. [40] CUI Chao,SANG Nong,GAO Changxin,et al.Vehicle re-identification by fusing multiple deep neural networks[C]//Proceedings of International Conference on Image Processing Theory,Tools and Applications.Washington D.C.,USA:IEEE Press,2017:1-6. [41] WANG Zhongdao,TANG Luming,LIU Xihui,et al.Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2017:379-387. [42] LIU Xiaobin,ZHANG Shiliang,HUANG Qingming,et al.RAM:a region-aware deep model for vehicle re-identification[C]//Proceedings of IEEE International Conference on Multimedia and Expo.Washington D.C.,USA:IEEE Press,2018:1-6. [43] HE Bing,LI Jia,ZHAO Yifan,et al.Part-regularized near-duplicate vehicle re-identification[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2019:3997-4005. [44] REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:779-788. [45] ZHANG Xinyu,ZHANG Rufeng,CAO Jiewei,et al.Part-guided attention learning for vehicle re-identification[EB/OL].[2019-11-15].https://arxiv.org/pdf/1909.06023.pdf. [46] GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems.New York,USA:ACM Press,2014:2672-2680. [47] RADFORD A,METZ L,CHINTALA S.Unsupervised representation learning with deep convolutional generative ad-versarial networks[EB/OL].[2019-11-15].https://arxiv.org/pdf/1511.06434.pdf. [48] ZHU J Y,PARK T,ISOLA P,et al.Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2017:2223-2232. [49] LUO Hao,JIANG Wei,FAN Xing,et al.A survey on deep learning based person re-identification[J].Acta Automatica Sinica,2019,45(11):2032-2049.(in Chinese)罗浩,姜伟,范星,等.基于深度学习的行人再识别研究进展[J].自动化学报,2019,45(11):2032-2049. [50] ZHOU Yi,SHAO Ling.Aware attentive multi-view inference for vehicle re-identification[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:6489-6498. [51] LOU Yihang,BAI Yan,LIU Jun,et al.Embedding adversarial learning for vehicle re-identification[J].IEEE Transactions on Image Processing,2019,28(8):3794-3807. [52] LIU Haijun,CHENG Jian,WANG Wen,et al.Enhancing the discriminative feature learning for visible-thermal cross-modality person re-identification[J].Neurocomputing,2020,398:11-19. [53] WANG Guan'an,ZHANG Tianzhu,CHENG Jian,et al.RGB-infrared cross-modality person re-identification via joint pixel and feature alignment[EB/OL].[2019-11-15].https://arxiv.org/pdf/1907.09659.pdf. [54] WANG Zhixiang,WANG Zheng,ZHENG Yinqiang,et al.Learning to reduce dual-level discrepancy for infrared-visible person re-identification[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2019:618-626. |