{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T03:47:16Z","timestamp":1778125636157,"version":"3.51.4"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"32","license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100008982","name":"Qatar National Research Fund","doi-asserted-by":"publisher","award":["AICC03-0324-200005"],"award-info":[{"award-number":["AICC03-0324-200005"]}],"id":[{"id":"10.13039\/100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100019687","name":"Hamad bin Khalifa University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100019687","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In intelligent systems for real-time security and safety monitoring, the proliferation of surveillance cameras has fueled a growing interest in using deep learning-based artificial intelligence (AI) models for violence detection. Most current approaches consider violence detection as a video classification task, overlooking the fact that violent activities occur within relatively small spatiotemporal regions. Moreover, these activities depend on relationships among multiple such regions, making a single region analysis inadequate, especially for larger-scale violence. This paper proposes a novel temporal\u2013spatial attention framework inspired by human visual perception, which dynamically focuses on multiple informative regions across space and time. By learning where, when, and for how long to attend within a video, using dynamic three-dimensional attention prediction networks, the model captures complex patterns of violent behavior more effectively. Experiments on four public benchmark datasets and a real-world dataset created for this study demonstrate that the proposed approach outperforms existing methods in accuracy and interpretability.<\/jats:p>","DOI":"10.1007\/s00521-025-11641-4","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T11:16:02Z","timestamp":1758021362000},"page":"26689-26709","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A temporal\u2013spatial deep learning framework leveraging dynamic 3D attention maps for violence detection"],"prefix":"10.1007","volume":"37","author":[{"given":"Elizabeth B.","family":"Varghese","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Almiqdad","family":"Elzein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marwa","family":"Qaraqe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"11641_CR1","doi-asserted-by":"crossref","unstructured":"Wu P, Liu X, Liu J (2022) Weakly supervised audio-visual violence detection. IEEE Transactions on Multimedia","DOI":"10.1109\/TMM.2022.3147369"},{"key":"11641_CR2","doi-asserted-by":"publisher","first-page":"106173","DOI":"10.1016\/j.engappai.2023.106173","volume":"123","author":"W Ullah","year":"2023","unstructured":"Ullah W, Hussain T, Ullah FUM, Lee MY, Baik SW (2023) Transcnn: Hybrid cnn and transformer mechanism for surveillance anomaly detection. Eng Appl Artif Intell 123:106173","journal-title":"Eng Appl Artif Intell"},{"key":"11641_CR3","first-page":"18","volume":"19","author":"T Ainsworth","year":"2002","unstructured":"Ainsworth T (2002) Buyer beware. Security Oz 19:18\u201326","journal-title":"Security Oz"},{"key":"11641_CR4","doi-asserted-by":"crossref","unstructured":"Bermejo\u00a0Nievas E, Deniz\u00a0Suarez O, Bueno\u00a0Garc\u00eda G, Sukthankar R (2011) Violence detection in video using computer vision techniques. In: Computer Analysis of Images and Patterns: 14th International Conference, CAIP 2011, Seville, Spain, August 29-31, 2011, Proceedings, Part II 14, pp. 332\u2013339. Springer","DOI":"10.1007\/978-3-642-23678-5_39"},{"key":"11641_CR5","doi-asserted-by":"crossref","unstructured":"Hassner T, Itcher Y, Kliper-Gross O (2012) Violent flows: real-time detection of violent crowd behavior. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1\u20136. IEEE","DOI":"10.1109\/CVPRW.2012.6239348"},{"key":"11641_CR6","doi-asserted-by":"publisher","first-page":"7379","DOI":"10.1007\/s11042-014-1984-4","volume":"74","author":"C-H Demarty","year":"2015","unstructured":"Demarty C-H, Penet C, Soleymani M, Gravier G (2015) Vsd, a public dataset for the detection of violent scenes in movies: design, annotation, analysis and evaluation. Multimed Tools Appl 74:7379\u20137404","journal-title":"Multimed Tools Appl"},{"key":"11641_CR7","doi-asserted-by":"crossref","unstructured":"Mohammadi S, Perina A, Kiani H, Murino V (2016) Angry crowds: Detecting violent events in videos. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part VII 14, pp. 3\u201318. Springer","DOI":"10.1007\/978-3-319-46478-7_1"},{"key":"11641_CR8","doi-asserted-by":"crossref","unstructured":"Sudhakaran S, Lanz O (2017) Learning to detect violent videos using convolutional long short-term memory. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1\u20136. IEEE","DOI":"10.1109\/AVSS.2017.8078468"},{"key":"11641_CR9","doi-asserted-by":"crossref","unstructured":"Hanson A, Pnvr K, Krishnagopal S, Davis L (2018) Bidirectional convolutional lstm for the detection of violence in videos. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops, pp. 0","DOI":"10.1007\/978-3-030-11012-3_24"},{"key":"11641_CR10","doi-asserted-by":"crossref","unstructured":"Perez M, Kot AC, Rocha A (2019) Detection of real-world fights in surveillance videos. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2662\u20132666. IEEE","DOI":"10.1109\/ICASSP.2019.8683676"},{"key":"11641_CR11","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1080\/135062800394667","volume":"7","author":"R Rensink","year":"2000","unstructured":"Rensink R (2000) The dynamic representation of scenes. Vis Cogn 7:17\u201342","journal-title":"Vis Cogn"},{"key":"11641_CR12","doi-asserted-by":"publisher","first-page":"16439","DOI":"10.1007\/s00521-021-06239-5","volume":"33","author":"Y Hou","year":"2021","unstructured":"Hou Y, Yu H, Zhou D, Wang P, Ge H, Zhang J, Zhang Q (2021) Local-aware spatio-temporal attention network with multi-stage feature fusion for human action recognition. Neural Comput Appl 33:16439\u201316450","journal-title":"Neural Comput Appl"},{"key":"11641_CR13","doi-asserted-by":"crossref","unstructured":"Song S, Lan C, Xing J, Zeng W, Liu J (2017) An end-to-end spatio-temporal attention model for human action recognition from skeleton data. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31","DOI":"10.1609\/aaai.v31i1.11212"},{"key":"11641_CR14","doi-asserted-by":"crossref","unstructured":"Fu J, Zheng H, Mei T (2017) Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4438\u20134446","DOI":"10.1109\/CVPR.2017.476"},{"key":"11641_CR15","unstructured":"Xiong C, Merity S, Socher R (2016) Dynamic memory networks for visual and textual question answering. In: International Conference on Machine Learning, pp. 2397\u20132406. PMLR"},{"key":"11641_CR16","doi-asserted-by":"crossref","unstructured":"Xu H, Saenko K (2016) Ask, attend and answer: Exploring question-guided spatial attention for visual question answering. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part VII 14, pp. 451\u2013466. Springer","DOI":"10.1007\/978-3-319-46478-7_28"},{"key":"11641_CR17","unstructured":"Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473"},{"key":"11641_CR18","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.engappai.2019.04.012","volume":"82","author":"MS Zitouni","year":"2019","unstructured":"Zitouni MS, Sluzek A, Bhaskar H (2019) Visual analysis of socio-cognitive crowd behaviors for surveillance: A survey and categorization of trends and methods. Eng Appl Artif Intell 82:294\u2013312","journal-title":"Eng Appl Artif Intell"},{"key":"11641_CR19","unstructured":"Sharma S, Kiros R, Salakhutdinov R (2015) Action recognition using visual attention. arXiv preprint arXiv:1511.04119"},{"key":"11641_CR20","unstructured":"Girdhar R, Ramanan D (2017) Attentional pooling for action recognition. Adv neural inf process syst 30"},{"key":"11641_CR21","unstructured":"Wang Y, Wang S, Tang J, O\u2019Hare N, Chang Y, Li B (2016) Hierarchical attention network for action recognition in videos. arXiv preprint arXiv:1607.06416"},{"key":"11641_CR22","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 CG (2018) Videolstm convolves, attends and flows for action recognition. Comput Vis Image Underst 166:41\u201350","journal-title":"Comput Vis Image Underst"},{"issue":"7","key":"11641_CR23","first-page":"2167","volume":"30","author":"Q Li","year":"2019","unstructured":"Li Q, Zhao X, He R, Huang K (2019) Recurrent prediction with spatio-temporal attention for crowd attribute recognition. IEEE Trans Circuits Syst Video Technol 30(7):2167\u20132177","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"11641_CR24","unstructured":"Torabi A, Sigal L (2017) Action classification and highlighting in videos. arXiv preprint arXiv:1708.09522"},{"issue":"8","key":"11641_CR25","doi-asserted-by":"publisher","first-page":"2405","DOI":"10.1109\/TCSVT.2018.2864148","volume":"29","author":"Z Yang","year":"2018","unstructured":"Yang Z, Li Y, Yang J, Luo J (2018) Action recognition with spatio-temporal visual attention on skeleton image sequences. IEEE Trans Circuits Syst Video Technol 29(8):2405\u20132415","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"11641_CR26","doi-asserted-by":"crossref","unstructured":"Zhou W, Zheng Z, Su T, Hu H (2023) Datran: Dual attention transformer for multi-label image classification. IEEE Transactions on Circuits and Systems for Video Technology","DOI":"10.1109\/TCSVT.2023.3284812"},{"key":"11641_CR27","doi-asserted-by":"crossref","unstructured":"Sun J, Wang H, Dong Q (2023) Hierarchical attention network for open-set fine-grained image recognition. IEEE Transactions on Circuits and Systems for Video Technology","DOI":"10.1109\/TCSVT.2023.3325001"},{"key":"11641_CR28","doi-asserted-by":"crossref","unstructured":"Wang Y, Yue Y, Lin Y, Jiang H, Lai Z, Kulikov V, Orlov N, Shi H, Huang G (2022) Adafocus v2: End-to-end training of spatial dynamic networks for video recognition. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20030\u201320040. IEEE","DOI":"10.1109\/CVPR52688.2022.01943"},{"key":"11641_CR29","doi-asserted-by":"crossref","unstructured":"Peixoto B, Lavi B, Martin JPP, Avila S, Dias Z, Rocha A (2019) Toward subjective violence detection in videos. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8276\u20138280. IEEE","DOI":"10.1109\/ICASSP.2019.8682833"},{"key":"11641_CR30","doi-asserted-by":"crossref","unstructured":"Singh A, Patil D, Omkar S (2018) Eye in the sky: Real-time drone surveillance system (dss) for violent individuals identification using scatternet hybrid deep learning network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1629\u20131637","DOI":"10.1109\/CVPRW.2018.00214"},{"key":"11641_CR31","doi-asserted-by":"crossref","unstructured":"Hachiuma R, Sato F, Sekii T (2023) Unified keypoint-based action recognition framework via structured keypoint pooling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22962\u201322971","DOI":"10.1109\/CVPR52729.2023.02199"},{"key":"11641_CR32","doi-asserted-by":"crossref","unstructured":"Degardin B, Proen\u00e7a H (2020) Human activity analysis: Iterative weak\/self-supervised learning frameworks for detecting abnormal events. In: 2020 IEEE International Joint Conference on Biometrics (IJCB), pp. 1\u20137. IEEE","DOI":"10.1109\/IJCB48548.2020.9304905"},{"key":"11641_CR33","doi-asserted-by":"crossref","unstructured":"Akt\u0131 \u015e, Tataro\u011flu GA, Ekenel HK (2019) Vision-based fight detection from surveillance cameras. In: 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1\u20136. IEEE","DOI":"10.1109\/IPTA.2019.8936070"},{"key":"11641_CR34","unstructured":"Nievas EB, Suarez OD, Garcia GB, Sukthankar R (2011) Hockey fight detection dataset. In: Computer Analysis of Images and Patterns, Springer, Heidelberg, pp 332\u2013339"},{"key":"11641_CR35","doi-asserted-by":"crossref","unstructured":"Soliman MM, Kamal MH, Nashed MAE-M, Mostafa YM, Chawky BS, Khattab D (2019) Violence recognition from videos using deep learning techniques. In: 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 80\u201385. IEEE","DOI":"10.1109\/ICICIS46948.2019.9014714"},{"issue":"1","key":"11641_CR36","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1080\/00335558008248231","volume":"32","author":"MI Posner","year":"1980","unstructured":"Posner MI (1980) Orienting of attention. Q J exp psychol 32(1):3\u201325","journal-title":"Q J exp psychol"},{"issue":"5","key":"11641_CR37","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1167\/11.5.13","volume":"11","author":"H Strasburger","year":"2011","unstructured":"Strasburger H, Rentschler I, J\u00fcttner M (2011) Peripheral vision and pattern recognition: a review. J Vis 11(5):13\u201313","journal-title":"J Vis"},{"issue":"3","key":"11641_CR38","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.tics.2013.01.010","volume":"17","author":"SL Franconeri","year":"2013","unstructured":"Franconeri SL, Alvarez GA, Cavanagh P (2013) Flexible cognitive resources: competitive content maps for attention and memory. Trends Cogn Sci 17(3):134\u2013141","journal-title":"Trends Cogn Sci"},{"issue":"8","key":"11641_CR39","doi-asserted-by":"publisher","first-page":"5359","DOI":"10.1109\/TII.2021.3116377","volume":"18","author":"FUM Ullah","year":"2021","unstructured":"Ullah FUM, Muhammad K, Haq IU, Khan N, Heidari AA, Baik SW, Albuquerque VHC (2021) Ai-assisted edge vision for violence detection in iot-based industrial surveillance networks. IEEE Trans Industr Inf 18(8):5359\u20135370","journal-title":"IEEE Trans Industr Inf"},{"key":"11641_CR40","doi-asserted-by":"crossref","unstructured":"Ravanbakhsh M, Mousavi H, Nabi M, Marcenaro L, Regazzoni C (2018) Fast but not deep: Efficient crowd abnormality detection with local binary tracklets. In: 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1\u20136. IEEE","DOI":"10.1109\/AVSS.2018.8639172"},{"key":"11641_CR41","doi-asserted-by":"crossref","unstructured":"Mousavi H, Mohammadi S, Perina A, Chellali R, Murino V (2015) Analyzing tracklets for the detection of abnormal crowd behavior. In: 2015 IEEE Winter Conference on Applications of Computer Vision, pp. 148\u2013155. IEEE","DOI":"10.1109\/WACV.2015.27"},{"key":"11641_CR42","doi-asserted-by":"crossref","unstructured":"Li J, Jiang X, Sun T, Xu K (2019) Efficient violence detection using 3d convolutional neural networks. In: 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1\u20138. IEEE","DOI":"10.1109\/AVSS.2019.8909883"},{"key":"11641_CR43","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.imavis.2016.01.006","volume":"48","author":"Y Gao","year":"2016","unstructured":"Gao Y, Liu H, Sun X, Wang C, Liu Y (2016) Violence detection using oriented violent flows. Image Vis Comput 48:37\u201341","journal-title":"Image Vis Comput"},{"issue":"67","key":"11641_CR44","doi-asserted-by":"publisher","first-page":"40","DOI":"10.4114\/intartif.vol24iss67pp40-50","volume":"24","author":"JP Oliveira Lima","year":"2021","unstructured":"Oliveira Lima JP, Figueiredo CMS (2021) A temporal fusion approach for video classification with convolutional and lstm neural networks applied to violence detection. Intel Artif 24(67):40\u201350","journal-title":"Intel Artif"},{"key":"11641_CR45","doi-asserted-by":"crossref","unstructured":"Li Y, Ji B, Shi X, Zhang J, Kang B, Wang L (2020) Tea: Temporal excitation and aggregation for action recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 909\u2013918","DOI":"10.1109\/CVPR42600.2020.00099"},{"key":"11641_CR46","doi-asserted-by":"crossref","unstructured":"Meng Y, Lin C-C, Panda R, Sattigeri P, Karlinsky L, Oliva A, Saenko K, Feris R (2020) Ar-net: Adaptive frame resolution for efficient action recognition. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part VII 16, pp. 86\u2013104. Springer","DOI":"10.1007\/978-3-030-58571-6_6"},{"key":"11641_CR47","unstructured":"Carreira J, Noland E, Banki-Horvath A, Hillier C, Zisserman A (2018) A short note about kinetics-600. arXiv preprint arXiv:1808.01340"},{"key":"11641_CR48","doi-asserted-by":"crossref","unstructured":"Dong Z, Qin J, Wang Y (2016) Multi-stream deep networks for person to person violence detection in videos. In: Pattern Recognition: 7th Chinese Conference, CCPR 2016, Chengdu, China, November 5-7, 2016, Proceedings, Part I 7, pp. 517\u2013531. Springer","DOI":"10.1007\/978-981-10-3002-4_43"},{"key":"11641_CR49","doi-asserted-by":"crossref","unstructured":"Xu L, Gong C, Yang J, Wu Q, Yao L (2014) Violent video detection based on mosift feature and sparse coding. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3538\u20133542. IEEE","DOI":"10.1109\/ICASSP.2014.6854259"},{"key":"11641_CR50","doi-asserted-by":"crossref","unstructured":"Carreira J, Zisserman A (2017) Quo vadis, action recognition? a new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299\u20136308","DOI":"10.1109\/CVPR.2017.502"},{"key":"11641_CR51","doi-asserted-by":"crossref","unstructured":"Gao M, Jiang J, Ma L, Zhou S, Zou G, Pan J, Liu Z (2019) Violent crowd behavior detection using deep learning and compressive sensing. In: 2019 Chinese Control And Decision Conference (CCDC), pp. 5329\u20135333. IEEE","DOI":"10.1109\/CCDC.2019.8832598"},{"key":"11641_CR52","doi-asserted-by":"crossref","unstructured":"Varghese EB, Thampi SM (2018) A deep learning approach to predict crowd behavior based on emotion. In: Smart Multimedia: First International Conference, ICSM 2018, Toulon, France, August 24\u201326, 2018, Revised Selected Papers 1, pp. 296\u2013307. Springer","DOI":"10.1007\/978-3-030-04375-9_25"},{"issue":"2","key":"11641_CR53","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1109\/TAFFC.2020.2987021","volume":"13","author":"EB Varghese","year":"2020","unstructured":"Varghese EB, Thampi SM, Berretti S (2020) A psychologically inspired fuzzy cognitive deep learning framework to predict crowd behavior. IEEE Trans Affect Comput 13(2):1005\u20131022","journal-title":"IEEE Trans Affect Comput"},{"issue":"3","key":"11641_CR54","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1109\/TCSVT.2016.2589858","volume":"27","author":"T Zhang","year":"2016","unstructured":"Zhang T, Jia W, He X, Yang J (2016) Discriminative dictionary learning with motion weber local descriptor for violence detection. IEEE Trans Circuits Syst Video Technol 27(3):696\u2013709","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"6","key":"11641_CR55","doi-asserted-by":"publisher","first-page":"2216","DOI":"10.3390\/s22062216","volume":"22","author":"R Vijeikis","year":"2022","unstructured":"Vijeikis R, Raudonis V, Dervinis G (2022) Efficient violence detection in surveillance. Sensors 22(6):2216","journal-title":"Sensors"},{"key":"11641_CR56","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11641_CR57","doi-asserted-by":"crossref","unstructured":"Su Y, Lin G, Zhu J, Wu Q (2020) Human interaction learning on 3d skeleton point clouds for video violence recognition. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part IV 16, pp. 74\u201390. Springer","DOI":"10.1007\/978-3-030-58548-8_5"},{"key":"11641_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-021-01264-9","volume":"33","author":"D Freire-Obreg\u00f3n","year":"2022","unstructured":"Freire-Obreg\u00f3n D, Barra P, Castrill\u00f3n-Santana M, Marsico MD (2022) Inflated 3d convnet context analysis for violence detection. Mach Vis Appl 33:1\u201313","journal-title":"Mach Vis Appl"},{"key":"11641_CR59","doi-asserted-by":"crossref","unstructured":"Lin J, Gan C, Han S (2019) Tsm: Temporal shift module for efficient video understanding. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7083\u20137093","DOI":"10.1109\/ICCV.2019.00718"},{"key":"11641_CR60","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"11641_CR61","doi-asserted-by":"crossref","unstructured":"Ciampi L, Santiago C, Costeira JP, Falchi F, Gennaro C, Amato G (2023) Unsupervised domain adaptation for video violence detection in the wild. In: IMPROVE, pp. 37\u201346","DOI":"10.5220\/0011965300003497"},{"key":"11641_CR62","doi-asserted-by":"publisher","first-page":"43796","DOI":"10.1109\/ACCESS.2024.3380192","volume":"12","author":"M Khan","year":"2024","unstructured":"Khan M, El Saddik A, Gueaieb W, De Masi G, Karray F (2024) Vd-net: An edge vision-based surveillance system for violence detection. IEEE Access 12:43796\u201343808","journal-title":"IEEE Access"},{"key":"11641_CR63","doi-asserted-by":"crossref","unstructured":"Arnab A, Dehghani M, Heigold G, Sun C, Lu\u010di\u0107 M, Schmid C (2021) Vivit: A video vision transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6836\u20136846","DOI":"10.1109\/ICCV48922.2021.00676"},{"issue":"21","key":"11641_CR64","doi-asserted-by":"publisher","first-page":"10709","DOI":"10.1007\/s10489-024-05775-6","volume":"54","author":"M Qaraqe","year":"2024","unstructured":"Qaraqe M, Yang YD, Varghese EB, Basaran E, Elzein A (2024) Crowd behavior detection: leveraging video swin transformer for crowd size and violence level analysis. Appl Intell 54(21):10709\u201310730","journal-title":"Appl Intell"},{"key":"11641_CR65","doi-asserted-by":"crossref","unstructured":"Tran D, Wang H, Torresani L, Ray J, LeCun Y, Paluri M (2018) A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6450\u20136459","DOI":"10.1109\/CVPR.2018.00675"},{"key":"11641_CR66","doi-asserted-by":"crossref","unstructured":"Neimark D, Bar O, Zohar M, Asselmann D (2021) Video transformer network. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3163\u20133172","DOI":"10.1109\/ICCVW54120.2021.00355"},{"key":"11641_CR67","doi-asserted-by":"crossref","unstructured":"Liu Z, Ning J, Cao Y, Wei Y, Zhang Z, Lin S, Hu H (2022) Video swin transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3202\u20133211","DOI":"10.1109\/CVPR52688.2022.00320"},{"key":"11641_CR68","doi-asserted-by":"crossref","unstructured":"Abdali AR (2021) Data efficient video transformer for violence detection. In: 2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), pp. 195\u2013199. IEEE","DOI":"10.1109\/COMNETSAT53002.2021.9530829"},{"key":"11641_CR69","doi-asserted-by":"crossref","unstructured":"Aladdin AM, Rashid TA (2025) Leo: Lagrange elementary optimization. Neural Comput Appl, pp 1\u201333","DOI":"10.1007\/s00521-025-11225-2"},{"key":"11641_CR70","unstructured":"Gildenblat J (2021) contributors: PyTorch library for CAM methods. GitHub"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11641-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11641-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11641-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T20:16:18Z","timestamp":1761077778000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11641-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"references-count":70,"journal-issue":{"issue":"32","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["11641"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11641-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,16]]},"assertion":[{"value":"27 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare they have no conflict of interest to report regarding the present study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No ethics approval was required for the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"All authors have approved the manuscript and agree with its publication.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}