{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:19:57Z","timestamp":1781587197399,"version":"3.54.5"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T00:00:00Z","timestamp":1676332800000},"content-version":"vor","delay-in-days":44,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2022JBQY007"],"award-info":[{"award-number":["2022JBQY007"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015282","name":"China Academy of Railway Sciences","doi-asserted-by":"publisher","award":["2021IMXM04"],"award-info":[{"award-number":["2021IMXM04"]}],"id":[{"id":"10.13039\/501100015282","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>Vehicles transporting hazardous material (HAZMAT) pose a severe threat to highway safety, especially in road tunnels. Vehicle reidentification is essential for identifying and warning abnormal states of HAZMAT vehicles in road tunnels. However, there is still no public dataset for benchmarking this task. To this end, this work releases a real\u2010world tunnel HAZMAT vehicle reidentification dataset, VisInt\u2010THV\u2010ReID, including 10,048 images with 865 HAZMAT vehicles and their spatiotemporal information. A method based on multimodal information fusion is proposed to realize vehicle reidentification by fusing vehicle appearance and spatiotemporal information. We design a spatiotemporal similarity determination method for vehicles based on the spatiotemporal law of vehicles in tunnels. Compared with other reidentification methods based on multimodal information fusion, i.e., PROVID, Visual\u2009+\u2009ST, and Siamese\u2010CNN, experimental results show that our approach significantly improves the vehicle reidentification recognition precision.<\/jats:p>","DOI":"10.1155\/2023\/3677387","type":"journal-article","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T18:06:03Z","timestamp":1676397963000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["HAZMAT Vehicle Reidentification in Road Tunnels Based on the Fusion of Appearance and Spatiotemporal Information"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0192-825X","authenticated-orcid":false,"given":"Lei","family":"Jia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaobao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianzhu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haomin","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3860-4809","authenticated-orcid":false,"given":"Qingyong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,2,14]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2008.05.011"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1504\/ijhvs.2010.035988"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/e24040466"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/math9243162"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.11.069"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2015.06.024"},{"key":"e_1_2_10_7_2","volume-title":"MultiMedia Modeling","author":"Zhong X.","year":"2018"},{"key":"e_1_2_10_8_2","doi-asserted-by":"crossref","unstructured":"ShenY. XiaoT. LiH. YiS. andWangX. Learning deep neural networks for vehicle Re-ID with visual-spatio-temporal path proposals Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV) October 2017 Venice Italy IEEE.","DOI":"10.1109\/ICCV.2017.210"},{"key":"e_1_2_10_9_2","doi-asserted-by":"crossref","unstructured":"JiangN. XuY. ZhouZ. andWuW. Multi-attribute driven vehicle Re-identification with spatial-temporal Re-ranking Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP) October 2018 Athens Greece IEEE.","DOI":"10.1109\/ICIP.2018.8451776"},{"key":"e_1_2_10_10_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. andSunJ. Deep residual learning for image recognition Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2016 Las Vegas NV USA IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2020.3006047"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2012.02.006"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"key":"e_1_2_10_14_2","first-page":"232","article-title":"Real-time Vehicle Matching for Multi-Camera Tunnel Surveillance","author":"Jela\u010da V.","year":"2011","journal-title":"Real-Time Image and Video Processing 2011"},{"key":"e_1_2_10_15_2","doi-asserted-by":"crossref","unstructured":"ChenH. T. ChuM. C. ChouC. L. LeeS. Y. andLinB. S. Multi-camera vehicle identification in tunnel surveillance system Proceedings of the 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) June 2015 Turin Italy IEEE.","DOI":"10.1109\/ICMEW.2015.7169793"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2009.154"},{"key":"e_1_2_10_17_2","doi-asserted-by":"crossref","unstructured":"LiuX. LiuW. MaH. andFuH. Large-scale vehicle re-identification in urban surveillance videos Proceedings of the 2016 IEEE International Conference on Multimedia and Expo (ICME) July 2016 Seattle WA USA IEEE.","DOI":"10.1109\/ICME.2016.7553002"},{"key":"e_1_2_10_18_2","doi-asserted-by":"crossref","unstructured":"ZhengL. WangS. ZhouW. andTianQ. Bayes merging of multiple vocabularies for scalable image retrieval Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition June 2014 Columbus OH USA IEEE.","DOI":"10.1109\/CVPR.2014.252"},{"key":"e_1_2_10_19_2","doi-asserted-by":"crossref","unstructured":"ZhengL. ShenL. TianL. WangS. WangJ. andTianQ. Scalable person Re-identification: a benchmark Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV) December 2015 Santiago Chile IEEE.","DOI":"10.1109\/ICCV.2015.133"},{"key":"e_1_2_10_20_2","doi-asserted-by":"crossref","unstructured":"SzegedyC. LiuW. JiaY. SermanetP. ReedS. AnguelovD. ErhanD. VanhouckeV. andRabinovichA. Going deeper with convolutions Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2015 Boston MA USA IEEE.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmm.2017.2751966"},{"key":"e_1_2_10_22_2","doi-asserted-by":"crossref","unstructured":"LuoH. GuY. LiaoX. LaiS. andJiangW. Bag of tricks and a strong baseline for deep person Re-identification Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) March 2019 Long Beach USA IEEE.","DOI":"10.1109\/CVPRW.2019.00190"},{"key":"e_1_2_10_23_2","doi-asserted-by":"crossref","unstructured":"SchroffF. KalenichenkoD. andPhilbinJ. FaceNet: a unified embedding for face recognition and clustering Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2015 Boston MA USA IEEE.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"e_1_2_10_24_2","doi-asserted-by":"crossref","unstructured":"ZhengL. ZhangH. SunS. ChandrakerM. YangY. andTianQ. Person Re-identification in the wild Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) July 2017 Honolulu HI USA 3346\u20133355.","DOI":"10.1109\/CVPR.2017.357"},{"key":"e_1_2_10_25_2","doi-asserted-by":"crossref","unstructured":"LiuH. TianY. WangY. PangL. andHuangT. Deep relative distance learning: tell the difference between similar vehicles Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2016 Las Vegas NV USA IEEE.","DOI":"10.1109\/CVPR.2016.238"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2023\/3677387.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2023\/3677387.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2023\/3677387","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T12:52:41Z","timestamp":1722948761000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2023\/3677387"}},"subtitle":[],"editor":[{"given":"Nian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["10.1155\/2023\/3677387"],"URL":"https:\/\/doi.org\/10.1155\/2023\/3677387","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]},"assertion":[{"value":"2022-06-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-10-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"3677387"}}