{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:35:29Z","timestamp":1760232929086,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2019YFB210 2200","61872025","RP\/ESCA-03\/2020","SKLSDE-2021ZX-03"],"award-info":[{"award-number":["2019YFB210 2200","61872025","RP\/ESCA-03\/2020","SKLSDE-2021ZX-03"]}]},{"name":"National Natural Science Foundation of China","award":["2019YFB210 2200","61872025","RP\/ESCA-03\/2020","SKLSDE-2021ZX-03"],"award-info":[{"award-number":["2019YFB210 2200","61872025","RP\/ESCA-03\/2020","SKLSDE-2021ZX-03"]}]},{"name":"Macao Polytechnic University","award":["2019YFB210 2200","61872025","RP\/ESCA-03\/2020","SKLSDE-2021ZX-03"],"award-info":[{"award-number":["2019YFB210 2200","61872025","RP\/ESCA-03\/2020","SKLSDE-2021ZX-03"]}]},{"name":"Open Fund of the State Key Laboratory of Software Development Environment","award":["2019YFB210 2200","61872025","RP\/ESCA-03\/2020","SKLSDE-2021ZX-03"],"award-info":[{"award-number":["2019YFB210 2200","61872025","RP\/ESCA-03\/2020","SKLSDE-2021ZX-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The problem of vehicle re-identification in surveillance scenarios has grown in popularity as a research topic. Deep learning has been successfully applied in re-identification tasks in the last few years due to its superior performance. However, deep learning approaches require a large volume of training data, and it is particularly crucial in vehicle re-identification tasks to have a sufficient amount of varying image samples for each vehicle. To collect and construct such a large and diverse dataset from natural environments is labor intensive. We offer a novel image sample synthesis framework to automatically generate new variants of training data by augmentation. First, we use an attention module to locate a local salient projection region in an image sample. Then, a lightweight convolutional neural network, the parameter agent network, is responsible for generating further image transformation states. Finally, an adversarial module is employed to ensure that the images in the dataset are distorted, while retaining their structural identities. This adversarial module helps to generate more appropriate and difficult training samples for vehicle re-identification. Moreover, we select the most difficult sample and update the parameter agent network accordingly to improve the performance. Our method draws on the adversarial networks strategy and the self-attention mechanism, which can dynamically decide the region selection and transformation degree of the synthesis images. Extensive experiments on the VeRi-776, VehicleID, and VERI-Wild datasets achieve good performance. Specifically, our method outperforms the state-of-the-art in MAP accuracy on VeRi-776 by 2.15%. Moreover, on VERI-Wil, a significant improvement of 7.15% is achieved.<\/jats:p>","DOI":"10.3390\/s22239539","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T05:50:52Z","timestamp":1670392252000},"page":"9539","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Learning for Data Synthesis: Joint Local Salient Projection and Adversarial Network Optimization for Vehicle Re-Identification"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6818-0437","authenticated-orcid":false,"given":"Yanbing","family":"Chen","sequence":"first","affiliation":[{"name":"Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China"},{"name":"School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0952-0961","authenticated-orcid":false,"given":"Wei","family":"Ke","sequence":"additional","affiliation":[{"name":"Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3942-0593","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9601-7824","authenticated-orcid":false,"given":"Cui","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2811-8962","authenticated-orcid":false,"given":"Hao","family":"Sheng","sequence":"additional","affiliation":[{"name":"Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China"},{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing 100191, China"},{"name":"Beihang Hangzhou Innovation Institute Yuhang, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9421-1014","authenticated-orcid":false,"given":"Zhang","family":"Xiong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, J., Cong, Y., Zhou, L., Tian, Z., and Qiu, J. (2022). Super-resolution-based part collaboration network for vehicle re-identification. World Wide Web, 1\u201320.","DOI":"10.1007\/s11280-022-01060-z"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"062001","DOI":"10.1088\/0957-0233\/20\/6\/062001","article-title":"Two-dimensional digital image correlation for in-plane displacement and strain measurement: A review","volume":"20","author":"Pan","year":"2009","journal-title":"Meas. Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"16600","DOI":"10.1109\/TITS.2022.3149657","article-title":"Efficient CityCam-to-Edge Cooperative Learning for Vehicle Counting in ITS","volume":"23","author":"Xu","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.cviu.2019.03.001","article-title":"A survey of advances in vision-based vehicle re-identification","volume":"182","author":"Khan","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1038\/nmeth.3707","article-title":"Deep learning","volume":"13","author":"Rusk","year":"2016","journal-title":"Nat. Methods"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_7","first-page":"6008","article-title":"Improved anti-occlusion object tracking algorithm using Unscented Rauch-Tung-Striebel smoother and kernel correlation filter","volume":"34","author":"Xia","year":"2022","journal-title":"J. King Saud-Univ.-Comput. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016, January 27\u201330). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V.N. (2018, January 12\u201315). Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, W., Huang, H., Zhang, Z., Chen, X., Huang, K., and Zhang, S. (2019, January 16\u201320). Towards rich feature discovery with class activation maps augmentation for person re-identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00148"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zheng, M., Karanam, S., Wu, Z., and Radke, R.J. (2019, January 16\u201320). Re-identification with consistent attentive siamese networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00588"},{"key":"ref_13","unstructured":"Zhang, Z., Lan, C., Zeng, W., Jin, X., and Chen, Z. (2019). Relation-aware global attention. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, X., Liu, W., Ma, H., and Fu, H. (2016, January 11\u201315). Large-scale vehicle re-identification in urban surveillance videos. Proceedings of the 2016 IEEE International Conference on Multimedia and Expo (ICME), Seattle, WA, USA.","DOI":"10.1109\/ICME.2016.7553002"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, H., Tian, Y., Yang, Y., Pang, L., and Huang, T. (2016, January 27\u201330). Deep relative distance learning: Tell the difference between similar vehicles. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.238"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lou, Y., Bai, Y., Liu, J., Wang, S., and Duan, L. (2019, January 16\u201320). Veri-wild: A large dataset and a new method for vehicle re-identification in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00335"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"108485","DOI":"10.1016\/j.asoc.2022.108485","article-title":"SCSTCF: Spatial-channel selection and temporal regularized correlation filters for visual tracking","volume":"118","author":"Zhang","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, Y., Yan, H., and Liu, J. (2017, January 17\u201320). Deep joint discriminative learning for vehicle re-identification and retrieval. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), BeiJing, China.","DOI":"10.1109\/ICIP.2017.8296310"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shen, Y., Xiao, T., Li, H., Yi, S., and Wang, X. (2017, January 22\u201329). Learning deep neural networks for vehicle re-id with visual-spatio-temporal path proposals. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.210"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, G., Yuan, Y., Chen, X., Li, J., and Zhou, X. (2018, January 22\u201326). Learning discriminative features with multiple granularities for person re-identification. Proceedings of the 26th ACM International Conference on Multimedia, Seoul, Republic of Korea.","DOI":"10.1145\/3240508.3240552"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.geomorph.2009.07.006","article-title":"Despeckling SRTM and other topographic data with a denoising algorithm","volume":"114","author":"Stevenson","year":"2010","journal-title":"Geomorphology"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Teng, S., Liu, X., Zhang, S., and Huang, Q. (2018). Scan: Spatial and channel attention network for vehicle re-identification. Pacific Rim Conference on Multimedia, Springer.","DOI":"10.1007\/978-3-030-00764-5_32"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhou, T., Tulsiani, S., Sun, W., Malik, J., and Efros, A.A. (2016). View synthesis by appearance flow. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46493-0_18"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., and Fei-Fei, L. (2016). Perceptual losses for real-time style transfer and super-resolution. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"ref_26","unstructured":"Odena, A., Olah, C., and Shlens, J. (2017, January 6\u201311). Conditional image synthesis with auxiliary classifier gans. Proceedings of the International Conference on Machine Learning, PMLR, Sydney, Australia."},{"key":"ref_27","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","unstructured":"Bowles, C., Chen, L., Guerrero, R., Bentley, P., Gunn, R., Hammers, A., Dickie, D.A., Hern\u00e1ndez, M.V., Wardlaw, J., and Rueckert, D. (2018). Gan augmentation: Augmenting training data using generative adversarial networks. arXiv."},{"key":"ref_29","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhu, X., Liu, Y., Li, J., Wan, T., and Qin, Z. (2018). Emotion classification with data augmentation using generative adversarial networks. Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer.","DOI":"10.1007\/978-3-319-93040-4_28"},{"key":"ref_31","unstructured":"Ruder, S. (2017). An overview of multi-task learning in deep neural networks. arXiv."},{"key":"ref_32","unstructured":"Luo, H., Chen, W., Xu, X., Gu, J., and Li, H. (2022, October 20). An Empirical Study of Vehicle Re-Identification on the AI City Challenge. Available online: https:\/\/arxiv.org\/abs\/2105.09701."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Melekhov, I., Kannala, J., and Rahtu, E. (2016, January 4\u20138). Siamese network features for image matching. Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico.","DOI":"10.1109\/ICPR.2016.7899663"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3324926","article-title":"A survey of zero-shot learning: Settings, methods, and applications","volume":"10","author":"Wang","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Schaefer, S., McPhail, T., and Warren, J. (August, January 30). Image deformation using moving least squares. Proceedings of the ACM SIGGRAPH: Applied Perception in Graphics & Visualization 2006 Papers, New York, NY, USA.","DOI":"10.1145\/1179352.1141920"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"120380","DOI":"10.1109\/ACCESS.2021.3107579","article-title":"A General Method for Generating Discrete Orthogonal Matrices","volume":"9","author":"Chan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_37","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Caesar\u2019s Palace, Las Vegas, NV, USA."},{"key":"ref_38","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision And Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jung, H., Choi, M.K., Jung, J., Lee, J.H., Kwon, S., and Young Jung, W. (2017, January 21\u201326). ResNet-based vehicle classification and localization in traffic surveillance systems. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, the Hawaii Convention Center, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.129"},{"key":"ref_41","unstructured":"Zhang, S., Choromanska, A., and LeCun, Y. (2014). Deep learning with elastic averaging SGD. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Chen, W., Yang, Y., and Wang, Z. (2020, January 14\u201319). In defense of the triplet loss again: Learning robust person re-identification with fast approximated triplet loss and label distillation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Online.","DOI":"10.1109\/CVPRW50498.2020.00185"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"He, S., Luo, H., Chen, W., Zhang, M., Zhang, Y., Wang, F., Li, H., and Jiang, W. (2020, January 14\u201319). Multi-domain learning and identity mining for vehicle re-identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Online.","DOI":"10.1109\/CVPRW50498.2020.00299"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Luo, H., Gu, Y., Liao, X., Lai, S., and Jiang, W. (2019, January 16\u201320). Bag of tricks and a strong baseline for deep person re-identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00190"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2597","DOI":"10.1109\/TMM.2019.2958756","article-title":"A strong baseline and batch normalization neck for deep person re-identification","volume":"22","author":"Luo","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TMM.2017.2751966","article-title":"Provid: Progressive and multimodal vehicle reidentification for large-scale urban surveillance","volume":"20","author":"Liu","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_47","unstructured":"Khorramshahi, P., Kumar, A., Peri, N., Rambhatla, S.S., Chen, J.C., and Chellappa, R. (November, January 27). A dual-path model with adaptive attention for vehicle re-identification. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kuma, R., Weill, E., Aghdasi, F., and Sriram, P. (2019, January 14\u201319). Vehicle re-identification: An efficient baseline using triplet embedding. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852059"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"34195","DOI":"10.1007\/s11042-020-09987-z","article-title":"Eliminating cross-camera bias for vehicle re-identification","volume":"81","author":"Peng","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"He, B., Li, J., Zhao, Y., and Tian, Y. (2019, January 16\u201320). Part-regularized near-duplicate vehicle re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00412"},{"key":"ref_51","unstructured":"Zheng, A., Lin, X., Li, C., He, R., and Tang, J. (2019). Attributes guided feature learning for vehicle re-identification. arXiv."},{"key":"ref_52","unstructured":"Tang, Z., Naphade, M., Birchfield, S., Tremblay, J., Hodge, W., Kumar, R., Wang, S., and Yang, X. (November, January 27). Pamtri: Pose-aware multi-task learning for vehicle re-identification using highly randomized synthetic data. Proceedings of the IEEE International Conference on Computer Vision, the COEX Convention Center, Seoul, Republic of Korea."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yao, Y., Zheng, L., Yang, X., Naphade, M., and Gedeon, T. (2019). Simulating content consistent vehicle datasets with attribute descent. arXiv.","DOI":"10.1007\/978-3-030-58539-6_46"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhou, Y., and Shao, L. (2018, January 18\u201322). Aware attentive multi-view inference for vehicle re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, the Calvin L. Rampton Salt Palace Convention Center, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00679"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wang, Z., Tang, L., Liu, X., Yao, Z., Yi, S., Shao, J., Yan, J., Wang, S., Li, H., and Wang, X. (2017, January 22\u201329). Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.49"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Yang, L., Luo, P., Change Loy, C., and Tang, X. (2015, January 7\u201312). A large-scale car dataset for fine-grained categorization and verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299023"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"162605","DOI":"10.1109\/ACCESS.2019.2948965","article-title":"Multi-Label-Based Similarity Learning for Vehicle Re-Identification","volume":"7","author":"Alfasly","year":"2019","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9539\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:35:05Z","timestamp":1760146505000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9539"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,6]]},"references-count":57,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239539"],"URL":"https:\/\/doi.org\/10.3390\/s22239539","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,12,6]]}}}