{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T08:09:54Z","timestamp":1761811794899,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032043382","type":"print"},{"value":"9783032043399","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-032-04339-9_14","type":"book-chapter","created":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T08:06:35Z","timestamp":1761811595000},"page":"217-228","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SwiNight: Class Imbalanced Night-Time Accident Detection with\u00a0Swin Transformer"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4433-9637","authenticated-orcid":false,"given":"Shrusti","family":"Porwal","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6885-1376","authenticated-orcid":false,"given":"Preety","family":"Singh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4577-6844","authenticated-orcid":false,"given":"Anukriti","family":"Bansal","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1604-3665","authenticated-orcid":false,"given":"Saumilya","family":"Gupta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3513-8971","authenticated-orcid":false,"given":"Kartikay","family":"Goel","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9435-0565","authenticated-orcid":false,"given":"Palakurthy","family":"Guneeth","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,31]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Aboah, A.: A vision-based system for traffic anomaly detection using deep learning and decision trees. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4207\u20134212 (2021)","DOI":"10.1109\/CVPRW53098.2021.00475"},{"key":"14_CR2","doi-asserted-by":"publisher","first-page":"59134","DOI":"10.1109\/ACCESS.2024.3387972","volume":"12","author":"VA Adewopo","year":"2024","unstructured":"Adewopo, V.A., Elsayed, N.: Smart city transportation: deep learning ensemble approach for traffic accident detection. IEEE Access 12, 59134\u201359147 (2024)","journal-title":"IEEE Access"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Bao, W., Yu, Q., Kong, Y.: Uncertainty-based traffic accident anticipation with spatio-temporal relational learning. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2682\u20132690 (2020)","DOI":"10.1145\/3394171.3413827"},{"key":"14_CR4","unstructured":"Bureau of Police Research & Development (BPR &D): Analysis of Road Accidents in India 2019 (2019). https:\/\/bro.gov.in\/WriteReadData\/linkimages\/5768690382-14.pdf"},{"key":"14_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/978-3-319-54190-7_9","volume-title":"Computer Vision \u2013 ACCV 2016","author":"F-H Chan","year":"2017","unstructured":"Chan, F.-H., Chen, Y.-T., Xiang, Yu., Sun, M.: Anticipating accidents in dashcam videos. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10114, pp. 136\u2013153. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-54190-7_9"},{"key":"14_CR6","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"14_CR7","doi-asserted-by":"publisher","unstructured":"Hajri, F., Fradi, H.: Vision transformers for road accident detection from dashboard cameras. In: 2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp.\u00a01\u20138 (2022). https:\/\/doi.org\/10.1109\/AVSS56176.2022.9959545","DOI":"10.1109\/AVSS56176.2022.9959545"},{"key":"14_CR8","doi-asserted-by":"publisher","first-page":"4806","DOI":"10.1109\/ACCESS.2019.2962617","volume":"8","author":"Y Ho","year":"2020","unstructured":"Ho, Y., Wookey, S.: The real-world-weight cross-entropy loss function: modeling the costs of mislabeling. IEEE Access 8, 4806\u20134813 (2020)","journal-title":"IEEE Access"},{"issue":"2","key":"14_CR9","first-page":"1","volume":"6","author":"X Huang","year":"2020","unstructured":"Huang, X., He, P., Rangarajan, A., Ranka, S.: Intelligent intersection: two-stream convolutional networks for real-time near-accident detection in traffic video. ACM Trans. Spat. Algor. Syst. (TSAS) 6(2), 1\u201328 (2020)","journal-title":"ACM Trans. Spat. Algor. Syst. (TSAS)"},{"issue":"5","key":"14_CR10","doi-asserted-by":"publisher","first-page":"429","DOI":"10.3233\/IDA-2002-6504","volume":"6","author":"N Japkowicz","year":"2002","unstructured":"Japkowicz, N., Stephen, S.: The class imbalance problem: a systematic study. Intell. Data Anal. 6(5), 429\u2013449 (2002)","journal-title":"Intell. Data Anal."},{"issue":"2","key":"14_CR11","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"T Lin","year":"2017","unstructured":"Lin, T.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318\u2013327 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Paul, A.R., Grace Mary\u00a0Kanaga, E.: Enhanced D-CNN architecture and centroid-based algorithm for real-time vehicle tracking and accident detection from surveillance videos. J. Intell. Fuzzy Syst. (Preprint), 1\u201314 (2024)","DOI":"10.3233\/JIFS-235911"},{"issue":"3","key":"14_CR14","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.icte.2021.11.004","volume":"8","author":"K Pawar","year":"2022","unstructured":"Pawar, K., Attar, V.: Deep learning based detection and localization of road accidents from traffic surveillance videos. ICT Express 8(3), 379\u2013387 (2022)","journal-title":"ICT Express"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Shah, A.P., Lamare, J.B., Nguyen-Anh, T., Hauptmann, A.: CADP: a novel dataset for CCTV traffic camera based accident analysis. In: 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp.\u00a01\u20139. IEEE (2018)","DOI":"10.1109\/AVSS.2018.8639160"},{"key":"14_CR16","unstructured":"Wali, R.: Xtreme margin: a tunable loss function for binary classification problems. arXiv Preprint arXiv:2211.00176 (2022)"},{"key":"14_CR17","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1109\/TIP.2024.3355812","volume":"33","author":"H Yu","year":"2024","unstructured":"Yu, H., Zhang, X., Wang, Y., Huang, Q., Yin, B.: Fine-grained accident detection: database and algorithm. IEEE Trans. Image Process. 33, 1059\u20131069 (2024)","journal-title":"IEEE Trans. Image Process."},{"key":"14_CR18","doi-asserted-by":"publisher","unstructured":"Zhong, Y., Wang, Z., Shi, X., Yang, J., Li, K.: Rfg-helad: a robust fine-grained network traffic anomaly detection model based on heterogeneous ensemble learning. IEEE Trans. Inf. Forensics Secur. 19, 5895\u20135910 (2024). https:\/\/doi.org\/10.1109\/TIFS.2024.3402439","DOI":"10.1109\/TIFS.2024.3402439"}],"container-title":["Communications in Computer and Information Science","Deep Learning Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04339-9_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T08:06:41Z","timestamp":1761811601000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04339-9_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783032043382","9783032043399"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04339-9_14","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"31 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DeLTA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Deep Learning Theory and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"delta2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/delta.scitevents.org\/?y=2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}