{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T17:47:16Z","timestamp":1768412836556,"version":"3.49.0"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030198220","type":"print"},{"value":"9783030198237","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-19823-7_23","type":"book-chapter","created":{"date-parts":[[2019,5,15]],"date-time":"2019-05-15T00:24:22Z","timestamp":1557879862000},"page":"282-291","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Detecting Violent Robberies in CCTV Videos Using Deep Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2911-8558","authenticated-orcid":false,"given":"Giorgio","family":"Morales","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4991-0446","authenticated-orcid":false,"given":"Itamar","family":"Salazar-Reque","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1685-2289","authenticated-orcid":false,"given":"Joel","family":"Telles","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5747-2795","authenticated-orcid":false,"given":"Daniel","family":"D\u00edaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,5,12]]},"reference":[{"key":"23_CR1","unstructured":"The Global Shapers Survey. http:\/\/shaperssurvey2017.org\/. Accessed 4 Feb 2019"},{"key":"23_CR2","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.patrec.2017.04.015","volume":"92","author":"AB Mabrouk","year":"2017","unstructured":"Mabrouk, A.B., Zagrouba, E.: Spatio-temporal feature using optical flow based distribution for violence detection. Pattern Recognit. Lett. 92, 62\u201367 (2017). https:\/\/doi.org\/10.1016\/j.patrec.2017.04.015","journal-title":"Pattern Recognit. Lett."},{"issue":"2\u20133","key":"23_CR3","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s11263-005-1838-7","volume":"64","author":"I Laptev","year":"2005","unstructured":"Laptev, I.: On space-time interest points. Int. J. Comput. Vis. 64(2\u20133), 107\u2013123 (2005). https:\/\/doi.org\/10.1007\/s11263-005-1838-7","journal-title":"Int. J. Comput. Vis."},{"key":"23_CR4","unstructured":"Deniz, O., Serrano, I., Bueno, G., Kim, T.K.: Fast violence detection in video. In: 2014 International Conference on Computer Vision Theory and Applications (VISAPP), pp. 478\u2013485. IEEE, Lisbon (2004)"},{"key":"23_CR5","series-title":"Lecture Notes in Electrical Engineering","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/978-981-13-2622-6_4","volume-title":"Computational Science and Technology","author":"NC Tay","year":"2019","unstructured":"Tay, N.C., Connie, T., Ong, T.S., Goh, K.O.M., Teh, P.S.: A robust abnormal behavior detection method using convolutional neural network. Computational Science and Technology. LNEE, vol. 481, pp. 37\u201347. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-13-2622-6_4"},{"key":"23_CR6","doi-asserted-by":"publisher","unstructured":"Wang, L., Qiao, Y., Tang, X.: Action recognition with trajectory-pooled deep-convolutional descriptors. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4305\u20134314 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7299059","DOI":"10.1109\/CVPR.2015.7299059"},{"key":"23_CR7","doi-asserted-by":"publisher","unstructured":"Wang, H., Schmid, C.: Action recognition with improved trajectories. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 3551\u20133558. IEEE, Sydney (2013). https:\/\/doi.org\/10.1109\/ICCV.2013.441","DOI":"10.1109\/ICCV.2013.441"},{"key":"23_CR8","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, pp. 568\u2013576. MIT Press, Montreal (2014)"},{"key":"23_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/978-3-319-68345-4_39","volume-title":"Computer Vision Systems","author":"Z Meng","year":"2017","unstructured":"Meng, Z., Yuan, J., Li, Z.: Trajectory-pooled deep convolutional networks for violence detection in videos. In: Liu, M., Chen, H., Vincze, M. (eds.) ICVS 2017. LNCS, vol. 10528, pp. 437\u2013447. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68345-4_39"},{"key":"23_CR10","doi-asserted-by":"publisher","first-page":"012044","DOI":"10.1088\/1742-6596\/844\/1\/012044","volume":"844","author":"P Zhou","year":"2017","unstructured":"Zhou, P., Ding, Q., Luo, H., Hou, X.: Violent interaction detection in video based on deep learning. J. Phys. Conf. Ser. 844, 012044 (2017)","journal-title":"J. Phys. Conf. Ser."},{"key":"23_CR11","doi-asserted-by":"publisher","unstructured":"Sudhakaran, S., Lanz, O.: Learning to detect violent videos using convolutional long short-term memory. In: 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, pp. 1\u20136 (2017). https:\/\/doi.org\/10.1109\/AVSS.2017.8078468","DOI":"10.1109\/AVSS.2017.8078468"},{"key":"23_CR12","doi-asserted-by":"publisher","unstructured":"Hassner, T., Itcher, I., Kliper-Gross, O.: Violent flows: real-time detection of violent crowd behavior. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. IEEE, Providence (2012). https:\/\/doi.org\/10.1109\/CVPRW.2012.6239348","DOI":"10.1109\/CVPRW.2012.6239348"},{"key":"23_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1007\/978-3-642-23678-5_39","volume-title":"Computer Analysis of Images and Patterns","author":"E Bermejo Nievas","year":"2011","unstructured":"Bermejo Nievas, E., Deniz Suarez, O., Bueno Garc\u00eda, G., Sukthankar, R.: Violence detection in video using computer vision techniques. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011. LNCS, vol. 6855, pp. 332\u2013339. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-23678-5_39"},{"issue":"1","key":"23_CR14","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/TPAMI.2013.111","volume":"36","author":"W Li","year":"2014","unstructured":"Li, W., Mahadevan, V., Vasconcelos, N.: Anomaly detection and localization in crowded scenes. IEEE Trans. Pattern. Anal. Mach. Intell. 36(1), 18\u201332 (2014)","journal-title":"IEEE Trans. Pattern. Anal. Mach. Intell."},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Sultani, W., Chen, C., Shah, M.: Real-world anomaly detection in surveillance videos. arXiv:1801.04264 (2018)","DOI":"10.1109\/CVPR.2018.00678"},{"key":"23_CR16","unstructured":"UNI-Crime Dataset. http:\/\/didt.inictel-uni.edu.pe\/dataset\/UNI-Crime_Dataset.rar. Accessed 25 Jan 2019"},{"key":"23_CR17","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8697\u20138710. IEEE, Salt Lake City (2018)","DOI":"10.1109\/CVPR.2018.00907"},{"key":"23_CR19","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Cortes, C., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1 (NIPS 2015), vol. 1, pp. 802\u2013810. MIT Press, Cambridge (2015)"},{"issue":"8","key":"23_CR20","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"23_CR21","unstructured":"Salehinejad, H., Sankar, S., Barfett, J., Colak, E., Valaee, S.: Recent advances in recurrent neural networks. arXiv:1801.01078 (2018)"},{"key":"23_CR22","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Miami (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Lee, G., Tai, Y., Kim, J.: Deep saliency with encoded low level distance map and high level features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 660\u2013668. IEEE, Las Vegas (2016)","DOI":"10.1109\/CVPR.2016.78"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Lan, Z., Zhu, Y., Hauptmann, A.G., Newsam, S.: Deep local video feature for action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, Honolulu (2017)","DOI":"10.1109\/CVPRW.2017.161"},{"key":"23_CR25","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.cviu.2017.10.011","volume":"166","author":"Z Li","year":"2018","unstructured":"Li, Z., Gavrilyuk, K., Gavves, E., Jain, M., Snoek, C.G.: Video LSTM convolves, attends and flows for action recognition. Comput. Vis. Image Underst. 166, 41\u201350 (2018). https:\/\/doi.org\/10.1016\/j.cviu.2017.10.011","journal-title":"Comput. Vis. Image Underst."},{"key":"23_CR26","doi-asserted-by":"publisher","first-page":"64","DOI":"10.3389\/fnbot.2018.00064","volume":"12","author":"T Liu","year":"2018","unstructured":"Liu, T., Stathaki, T.: Faster R-CNN for robust pedestrian detection using semantic segmentation network. Front. Neurorobot 12, 64 (2018). https:\/\/doi.org\/10.3389\/fnbot.2018.00064","journal-title":"Front. Neurorobot"},{"key":"23_CR27","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778. IEEE, Las Vegas (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR28","doi-asserted-by":"publisher","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1\u20139. IEEE, Boston (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"23_CR29","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inceptionv 4, Inception-Resnet and the impact of residual connections on learning. In: AAAI Conference on Artificial Intelligence, San Francisco (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"23_CR30","unstructured":"Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861 (2017)"},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Mengxiao, L., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. arXiv:1707.01083 (2017)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"23_CR32","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the International Conference on Learning Representations (ICLR 2015), San Diego (2015)"}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-19823-7_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:32:56Z","timestamp":1709832776000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-19823-7_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030198220","9783030198237"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-19823-7_23","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"value":"1868-4238","type":"print"},{"value":"1868-422X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"12 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Artificial Intelligence Applications and Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hersonissos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 May 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.aiai2019.eu\/","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":"Easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"101","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":"49","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":"6","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":"49% - 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":"2.3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}