{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T11:08:13Z","timestamp":1753355293976,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031451690"},{"type":"electronic","value":"9783031451706"}],"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\/978-3-031-45170-6_22","type":"book-chapter","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T13:03:02Z","timestamp":1699966982000},"page":"209-217","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Supervised Approach for\u00a0Efficient Video Anomaly Detection Using Transfer Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2882-5816","authenticated-orcid":false,"given":"Rangachary","family":"Kommanduri","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1694-9299","authenticated-orcid":false,"given":"Mrinmoy","family":"Ghorai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Aburakhia, S., Tayeh, T., Myers, R., Shami, A.: A transfer learning framework for anomaly detection using model of normality. In: 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 0055\u20130061. IEEE (2020)","DOI":"10.1109\/IEMCON51383.2020.9284916"},{"issue":"3","key":"22_CR2","doi-asserted-by":"publisher","first-page":"1967","DOI":"10.3233\/JIFS-169908","volume":"36","author":"S Bansod","year":"2019","unstructured":"Bansod, S., Nandedkar, A.: Transfer learning for video anomaly detection. J. Intell. Fuzzy Syst. 36(3), 1967\u20131975 (2019)","journal-title":"J. Intell. Fuzzy Syst."},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Chen, C.,et al.: Comprehensive regularization in a bi-directional predictive network for video anomaly detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 230\u2013238 (2022)","DOI":"10.1609\/aaai.v36i1.19898"},{"key":"22_CR4","doi-asserted-by":"publisher","first-page":"104391","DOI":"10.1016\/j.imavis.2022.104391","volume":"119","author":"A Guo","year":"2022","unstructured":"Guo, A., Guo, L., Zhang, R., Wang, Y., Gao, S.: Self-trained prediction model and novel anomaly score mechanism for video anomaly detection. Image Vis. Comput. 119, 104391 (2022)","journal-title":"Image Vis. Comput."},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Ionescu, R.T., Khan, F.S., Georgescu, M.I., Shao, L.: Object-centric auto-encoders and dummy anomalies for abnormal event detection in video. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7842\u20137851 (2019)","DOI":"10.1109\/CVPR.2019.00803"},{"issue":"4","key":"22_CR6","doi-asserted-by":"publisher","first-page":"5259","DOI":"10.1007\/s11042-021-11781-4","volume":"81","author":"R Lalit","year":"2022","unstructured":"Lalit, R., Purwar, R.K., Verma, S., Jain, A.: Crowd abnormality detection in video sequences using supervised convolutional neural network. Multimedia Tools Appl. 81(4), 5259\u20135277 (2022)","journal-title":"Multimedia Tools Appl."},{"key":"22_CR7","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.neucom.2019.08.044","volume":"369","author":"N Li","year":"2019","unstructured":"Li, N., Chang, F.: Video anomaly detection and localization via multivariate gaussian fully convolution adversarial autoencoder. Neurocomputing 369, 92\u2013105 (2019)","journal-title":"Neurocomputing"},{"key":"22_CR8","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.neucom.2021.01.097","volume":"439","author":"T Li","year":"2021","unstructured":"Li, T., Chen, X., Zhu, F., Zhang, Z., Yan, H.: Two-stream deep spatial-temporal auto-encoder for surveillance video abnormal event detection. Neurocomputing 439, 256\u2013270 (2021)","journal-title":"Neurocomputing"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Luo, W., Liu, W., Gao, S.: A revisit of sparse coding based anomaly detection in stacked RNN framework. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 341\u2013349 (2017)","DOI":"10.1109\/ICCV.2017.45"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Park, C., Cho, M., Lee, M., Lee, S.: FastANO: fast anomaly detection via spatio-temporal patch transformation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2249\u20132259 (2022)","DOI":"10.1109\/WACV51458.2022.00197"},{"issue":"4","key":"22_CR11","doi-asserted-by":"publisher","first-page":"1992","DOI":"10.1109\/TIP.2017.2670780","volume":"26","author":"M Sabokrou","year":"2017","unstructured":"Sabokrou, M., Fayyaz, M., Fathy, M., Klette, R.: Deep-cascade: cascading 3D deep neural networks for fast anomaly detection and localization in crowded scenes. IEEE Trans. Image Process. 26(4), 1992\u20132004 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Wan, B., Fang, Y., Xia, X., Mei, J.: Weakly supervised video anomaly detection via center-guided discriminative learning. In: 2020 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136. IEEE (2020)","DOI":"10.1109\/ICME46284.2020.9102722"},{"issue":"10","key":"22_CR13","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.1109\/TIFS.2019.2900907","volume":"14","author":"JT Zhou","year":"2019","unstructured":"Zhou, J.T., Du, J., Zhu, H., Peng, X., Liu, Y., Goh, R.S.M.: AnomalyNet: an anomaly detection network for video surveillance. IEEE Trans. Inf. Forensics Secur. 14(10), 2537\u20132550 (2019)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Zhou, W., Li, Y., Zhao, C.: Object-guided and motion-refined attention network for video anomaly detection. In: 2022 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136. IEEE (2022)","DOI":"10.1109\/ICME52920.2022.9859927"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45170-6_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T13:05:47Z","timestamp":1699967147000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45170-6_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031451690","9783031451706"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45170-6_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"4 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PReMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition and Machine Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"premi2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.isical.ac.in\/~premi23\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"311","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":"91","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":"0","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":"29% - 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":"3","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)"}}]}}