{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T04:11:40Z","timestamp":1748059900409,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031314162"},{"type":"electronic","value":"9783031314179"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-31417-9_8","type":"book-chapter","created":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T12:02:31Z","timestamp":1683374551000},"page":"96-107","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Vehicle ReID: Learning Robust Feature Using Vision Transformer and\u00a0Gradient Accumulation for\u00a0Vehicle Re-identification"],"prefix":"10.1007","author":[{"given":"Rishi","family":"Kishore","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8381-9702","authenticated-orcid":false,"given":"Nazia","family":"Aslam","sequence":"additional","affiliation":[]},{"given":"Maheshkumar H.","family":"Kolekar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,7]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"He, S., Luo, H., Wang, P., Wang, F., Li, H., Jiang, W.: Transreid: transformer-based object re-identification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15013\u201315022 (2021)","DOI":"10.1109\/ICCV48922.2021.01474"},{"key":"8_CR2","doi-asserted-by":"publisher","unstructured":"Liu, X., Liu, W., Mei, T., Ma, H.: Provid: progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans. Multimed. 20(3), 645\u2013658 (2017). https:\/\/doi.org\/10.10007\/1234567890","DOI":"10.10007\/1234567890"},{"issue":"3","key":"8_CR3","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1109\/TITS.2011.2114346","volume":"12","author":"Y Wen","year":"2011","unstructured":"Wen, Y., Lu, Y., Yan, J., Zhou, Z., von Deneen, K.M., Shi, P.: An algorithm for license plate recognition applied to intelligent transportation system. IEEE Trans. Intell. Transp. Syst. 12(3), 830\u2013845 (2011). https:\/\/doi.org\/10.1109\/TITS.2011.2114346","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Tang, Z., et al.: Pamtri: pose-aware multi-task learning for vehicle re-identification using highly randomized synthetic data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 211\u2013220 (2019)","DOI":"10.1109\/ICCV.2019.00030"},{"key":"8_CR5","doi-asserted-by":"publisher","unstructured":"Liu, H., Tian, Y., Wang, Y., Pang, L., Huang, T.: Deep relative distance learning: tell the difference between similar vehicles. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016, pp. 2167\u20132175 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.238","DOI":"10.1109\/CVPR.2016.238"},{"key":"8_CR6","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"8_CR7","unstructured":"Vaswani, A., et al.: Attention is all you need. Advances in neural information processing systems 30 (2017)"},{"issue":"12","key":"8_CR8","doi-asserted-by":"publisher","first-page":"7619","DOI":"10.1109\/TITS.2020.3006047","volume":"22","author":"Z Xiong","year":"2020","unstructured":"Xiong, Z., Li, M., Ma, Y., Xinkai, W.: Vehicle re-identification with image processing and car-following model using multiple surveillance cameras from urban arterials. IEEE Trans. Intell. Transp. Syst. 22(12), 7619\u20137630 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Zapletal, D., Herout, A.: Vehicle re-identification for automatic video traffic surveillance. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 25\u201331 (2016)","DOI":"10.1109\/CVPRW.2016.195"},{"key":"8_CR10","doi-asserted-by":"publisher","unstructured":"Sanchez, R.O., Flores, C., Horowitz, R., Rajagopal, R., Varaiya, P.: Arterial travel time estimation based on vehicle re-identification using magnetic sensors: performance analysis. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 997\u20131002 (2011). https:\/\/doi.org\/10.1109\/ITSC.2011.6083003","DOI":"10.1109\/ITSC.2011.6083003"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Sun, C.C., Ritchie, S.G., Joyce Tsai, K., Jayakrishnan, R.: Use of vehicle signature analysis and lexicographic optimization for vehicle reidentification on freeways. Transp. Res. Part C-emerging Technol. 7, 167\u2013185 (1999)","DOI":"10.1016\/S0968-090X(99)00018-2"},{"key":"8_CR12","doi-asserted-by":"publisher","unstructured":"Luo, H., Gu, Y., Liao, X., Lai, S., Jiang, W.: Bag of tricks and a strong baseline for deep person re-identification. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2019, pp. 1487\u20131495 (2019). https:\/\/doi.org\/10.1109\/CVPRW.2019.00190","DOI":"10.1109\/CVPRW.2019.00190"},{"key":"8_CR13","unstructured":"Li, Y., Liu, K., Jin, Y., Wang, T., Lin, W.: VARID: viewpoint-aware re-identification of vehicle based on triplet loss. IEEE Trans. Intell. Transp. Syst. (2020)"},{"key":"8_CR14","doi-asserted-by":"publisher","first-page":"2638","DOI":"10.1109\/TIP.2019.2950796","volume":"29","author":"X Liu","year":"2020","unstructured":"Liu, X., Zhang, S., Wang, X., Hong, R., Tian, Q.: Group-group loss-based global-regional feature learning for vehicle re-identification. IEEE Trans. Image Process. 29, 2638\u20132652 (2020). https:\/\/doi.org\/10.1109\/TIP.2019.2950796","journal-title":"IEEE Trans. Image Process."},{"issue":"7","key":"8_CR15","doi-asserted-by":"publisher","first-page":"3275","DOI":"10.1109\/TIP.2018.2819820","volume":"27","author":"Y Zhou","year":"2018","unstructured":"Zhou, Y., Liu, L., Shao, L.: Vehicle re-identification by deep hidden multi-view inference. IEEE Trans. Image Process. 27(7), 3275\u20133287 (2018). https:\/\/doi.org\/10.1109\/TIP.2018.2819820","journal-title":"IEEE Trans. Image Process."},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Aslam, N., Rai, P.K., Kolekar, M.H.: A3N: attention-based adversarial autoencoder network for detecting anomalies in video sequence. J. Visual Commun. Image Representation 87, 103598 (2022)","DOI":"10.1016\/j.jvcir.2022.103598"},{"key":"8_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1007\/978-3-319-46475-6_53","volume-title":"Computer Vision \u2013 ECCV 2016","author":"X Liu","year":"2016","unstructured":"Liu, X., Liu, W., Mei, T., Ma, H.: A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 869\u2013884. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_53"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Shao, L.: Cross-view GAN based vehicle generation for re-identification. In: BMVC, vol. 1, pp. 1\u201312 (September 2017)","DOI":"10.5244\/C.31.186"},{"key":"8_CR19","doi-asserted-by":"publisher","unstructured":"Zhouy, Y., Shao, L.: Viewpoint-aware attentive multi-view inference for vehicle re-identification. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2018, pp. 6489\u20136498 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00679","DOI":"10.1109\/CVPR.2018.00679"},{"key":"8_CR20","doi-asserted-by":"publisher","unstructured":"Wang, Z., et al.: Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In: IEEE International Conference on Computer Vision (ICCV) 2017, pp. 379\u2013387 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.49","DOI":"10.1109\/ICCV.2017.49"},{"key":"8_CR21","unstructured":"Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. Advances in neural information processing systems 29 (2016)"},{"key":"8_CR22","doi-asserted-by":"publisher","unstructured":"Chu, R., Sun, Y., Li, Y., Liu, Z., Zhang, C., Wei, Y.: Vehicle re-identification with viewpoint-aware metric learning. In: IEEE\/CVF International Conference on Computer Vision (ICCV) 2019, pp. 8281\u20138290 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00837","DOI":"10.1109\/ICCV.2019.00837"},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Aslam, N., Kolekar, M.H.: Unsupervised anomalous event detection in videos using spatio-temporal inter-fused autoencoder. Multimedia Tools and Applications, pp. 1\u201326 (2022)","DOI":"10.1007\/s11042-022-13496-6"},{"key":"8_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1007\/978-3-030-58536-5_20","volume-title":"Computer Vision \u2013 ECCV 2020","author":"T-S Chen","year":"2020","unstructured":"Chen, T.-S., Liu, C.-T., Wu, C.-W., Chien, S.-Y.: Orientation-aware vehicle re-identification with semantics-guided part attention network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 330\u2013346. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_20"},{"key":"8_CR25","doi-asserted-by":"publisher","unstructured":"Lou, Y., Bai, Y., Liu, J., Wang, S., Duan, L.: VERI-wild: a large dataset and a new method for vehicle re-identification in the wild. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2019, pp. 3230\u20133238 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00335","DOI":"10.1109\/CVPR.2019.00335"},{"key":"8_CR26","doi-asserted-by":"publisher","unstructured":"Khorramshahi, P., Kumar, A., Peri, N., Rambhatla, S.S., Chen, J.-C., Chellappa, R.: A dual-path model with adaptive attention for vehicle re-identification. In: IEEE\/CVF International Conference on Computer Vision (ICCV) 2019, pp. 6131\u20136140 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00623","DOI":"10.1109\/ICCV.2019.00623"},{"key":"8_CR27","doi-asserted-by":"publisher","unstructured":"Liu, X., Zhang, S., Huang, Q., Gao, W.: RAM: a region-aware deep model for vehicle re-identification. In: IEEE International Conference on Multimedia and Expo (ICME) 2018, pp. 1\u20136 (2018). https:\/\/doi.org\/10.1109\/ICME.2018.8486589","DOI":"10.1109\/ICME.2018.8486589"}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-31417-9_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T12:11:15Z","timestamp":1683375075000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-31417-9_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031314162","9783031314179"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-31417-9_8","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"7 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nagpur","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/vnit.ac.in\/cvip2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"110","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}