{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:03:23Z","timestamp":1743120203885,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031463167"},{"type":"electronic","value":"9783031463174"}],"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\/978-3-031-46317-4_22","type":"book-chapter","created":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T06:03:06Z","timestamp":1698472986000},"page":"271-282","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["WSAD-Net: Weakly Supervised Anomaly Detection in Untrimmed Surveillance Videos"],"prefix":"10.1007","author":[{"given":"Peng","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanning","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,29]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Sultani, W., Chen, C.,\u00a0 Shah, M.: Real-world anomaly detection in surveillance videos. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6479\u20136488. (2016)","DOI":"10.1109\/CVPR.2018.00678"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Luo, W., Liu, W.,\u00a0 Gao, S.: A revisit of sparse coding based anomaly detection in stacked RNN framework. In: Proceedings of the 2017 IEEE Conference on Computer Vision (ICCV), pp. 341\u2013349. (2017)","DOI":"10.1109\/ICCV.2017.45"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Liu, W., Luo, W., Lian, D.,\u00a0 Gao, S.: Future frame prediction for anomaly detection\u2013a new baseline. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6536\u20136545. (2018)","DOI":"10.1109\/CVPR.2018.00684"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Lv, H., Chen, C., Cui, Z., Xu, C., Li, Y., Yang, J.: Learning normal dynamics in videos with meta prototype network. In: Proceedings of the 2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.01517"},{"key":"22_CR5","unstructured":"Wu, P., Liu, J.,\u00a0 Shen, F.: A deep one-class neural network for anomalous event detection in complex scenes. IEEE Trans. Neural Netw. Learn. Syst. 31(7), 2609\u20132622 (2020)"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K.,\u00a0 Davis, L.S.: Learning temporal regularity in video sequences. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 733\u2013742. (2016)","DOI":"10.1109\/CVPR.2016.86"},{"key":"22_CR7","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1007\/978-3-030-58577-8_20","volume-title":"Computer Vision \u2013 ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXX","author":"Wu Peng","year":"2020","unstructured":"Peng, Wu., Liu, J., Shi, Y., Sun, Y., Shao, F., Zhaoyang, Wu., Yang, Z.: Not only look, but also listen: learning multimodal violence detection under weak supervision. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) Computer Vision \u2013 ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXX, pp. 322\u2013339. Springer International Publishing, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58577-8_20"},{"key":"22_CR8","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/978-3-319-46454-1_17","volume-title":"Computer Vision \u2013 ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V","author":"R De Geest","year":"2016","unstructured":"De Geest, R., Gavves, E., Ghodrati, A., Li, Z., Snoek, C., Tuytelaars, T.: Online action detection. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision \u2013 ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V, pp. 269\u2013284. Springer International Publishing, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_17"},{"key":"22_CR9","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1007\/978-3-030-01225-0_35","volume-title":"Computer Vision \u2013 ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part IV","author":"S Paul","year":"2018","unstructured":"Paul, S., Roy, S., Roy-Chowdhury, A.K.: W-TALC: weakly-supervised temporal activity localization and classification. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part IV, pp. 588\u2013607. Springer International Publishing, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_35"},{"key":"22_CR10","unstructured":"Kingma, D.P.,\u00a0 Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980. Retrieved from https:\/\/arxiv.org\/abs\/1412.6980. (2014)"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Wedel, A., Pock, T., Zach, C., Bischof, H.,\u00a0 Cremers, D.: An improved algorithm for TV-L1 optical flow. In: Proceedings of the International Dagstuhl-Seminar on Statistical and Geometrical Approaches to Visual Motion Analysis, pp. 23\u201345 (2009)","DOI":"10.1007\/978-3-642-03061-1_2"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the 2015 IEEE Conference on Computer Vision (ICCV), pp. 4489\u20134497 (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"22_CR13","unstructured":"Ruff, L., et al.: Deep one-class classification. In: Proceedings of the 2018 International Conference on Machine Learning (ICML), pp. 4393\u20134402 (2018)"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Kratz, L.,\u00a0 Nishino, K.: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1446\u20131453 (2009)","DOI":"10.1109\/CVPR.2009.5206771"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3449\u20133456 (2011)","DOI":"10.1109\/CVPR.2011.5995434"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Lu, C., Shi, J.,\u00a0 Jia, J.: Abnormal event detection at 150 fps in matlab. In: Proceedings of the 2013 IEEE Conference on Computer Vision (ICCV) pp. 2720\u20132727 (2013)","DOI":"10.1109\/ICCV.2013.338"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Leyva, R., Sanchez, V.,\u00a0 Li, C.-S.: Video anomaly detection with compact feature sets for online performance. IEEE Transactions on Image Processing (TIP), vol. 26(7), pp. 3463\u20133478 (2017)","DOI":"10.1109\/TIP.2017.2695105"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Xu, D., Ricci, E., Yan, Y., Song, J., Sebe, N.: Learning deep representations of appearance and motion for anomalous event detection. In: Proceedings of the 2015 British Machine Vision Conference (BMVC) (2015)","DOI":"10.5244\/C.29.8"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Carreira, J.,\u00a0 Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4724\u20134733 (2017)","DOI":"10.1109\/CVPR.2017.502"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1975\u20131981 (2010)","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Biswas, S., Babu, R.V.: Real time anomaly detection in H.264 compressed videos. In: Proceedings of the 2013 National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp.1\u20134 (2013)","DOI":"10.1109\/NCVPRIPG.2013.6776164"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Gunale, K.G., Mukherji, P. Deep learning with a spatiotemporal descriptor of appearance and motion estimation for video anomaly detection. J. Imaging (2018)","DOI":"10.3390\/jimaging4060079"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A. Learning deep features for discriminative localization. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Zhong, J., Li, N., Kong, W., Liu, S., Li, T.H., Li, G.: Graph convolutional label noise cleaner: train a plug-and-play action classifier for anomaly detection. In: Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1237\u20131246 (2019)","DOI":"10.1109\/CVPR.2019.00133"},{"key":"22_CR25","unstructured":"Pang, G., Shen, C., Cao, L., van den Hengel, A.: Deep learning for anomaly detection: a review. arXiv: 2007.02500.\u00a0\u00a0https:\/\/arxiv.org\/abs\/2007.02500. (2020)"},{"key":"22_CR26","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/978-3-031-19772-7_24","volume-title":"Computer Vision \u2013 ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part IV","author":"Z Yang","year":"2022","unstructured":"Yang, Z., Peng, Wu., Liu, J., Liu, X.: Dynamic local aggregation network with\u00a0adaptive clusterer for\u00a0anomaly detection. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision \u2013 ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part IV, pp. 404\u2013421. Springer Nature Switzerland, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19772-7_24"},{"key":"22_CR27","doi-asserted-by":"crossref","unstructured":"Wu, P., Liu, X., Liu, J.: Weakly supervised audio-visual violence detection. IEEE Trans. Multimedia (TMM)\u00a026, 1674\u20131685 (2022)","DOI":"10.1109\/TMM.2022.3147369"},{"key":"22_CR28","doi-asserted-by":"crossref","unstructured":"Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, J.W., Carneiro, G.: Weakly-supervised video anomaly detection with robust temporal feature magnitude learning. In: Proceedings of the 2021 IEEE International Conference on Computer Vision (CVPR), pp. 4975\u20134986 (2021)","DOI":"10.1109\/ICCV48922.2021.00493"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46317-4_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T06:13:03Z","timestamp":1698473583000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46317-4_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031463167","9783031463174"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46317-4_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":"29 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"22 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icig2023.csig.org.cn\/","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":"Conference Management Toolkit","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"409","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":"166","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":"41% - 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)"}}]}}