{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T11:10:47Z","timestamp":1788001847267,"version":"build-2784847793"},"reference-count":71,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T00:00:00Z","timestamp":1739232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP)","award":["RS-2019-II190231"],"award-info":[{"award-number":["RS-2019-II190231"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>As the global demand for surveillance cameras increases, the digital footage data also explicitly increases. Analyzing and extracting meaningful content from footage is a resource-depleting and laborious effort. The traditional video synopsis technique is used for constructing a small video by relocating the object in the time and space domains. However, it is computationally expensive, and the obtained synopsis suffers from jitter artifacts; thus, it cannot be hosted on a resource-constrained device. In this research, we propose a panoramic video synopsis framework to address and solve the problems of the efficient analysis of objects for better governance and storage. The surveillance system has multiple cameras sharing a common homography, which the proposed method leverages. The proposed method constructs a panorama by solving the broad viewpoints with significant deviations, collisions, and overlapping among the images. We embed a synopsis framework on the end device to reduce storage, networking, and computational costs. A neural network-based model stitches multiple camera feeds to obtain a panoramic structure from which only tubes with abnormal behavior were extracted and relocated in the space and time domains to construct a shorter video. Comparatively, the proposed model achieved a superior accuracy matching rate of 98.7% when stitching the images. The feature enhancement model also achieves better peak signal-to-noise ratio values, facilitating smooth synopsis construction.<\/jats:p>","DOI":"10.3390\/systems13020110","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T05:34:32Z","timestamp":1739252072000},"page":"110","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Panoramic Video Synopsis on Constrained Devices for Security Surveillance"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1118-1295","authenticated-orcid":false,"given":"Palash Yuvraj","family":"Ingle","sequence":"first","affiliation":[{"name":"Department of Computer and Information Security, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9585-8808","authenticated-orcid":false,"given":"Young-Gab","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Security, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,11]]},"reference":[{"key":"ref_1","unstructured":"Reinsel, D., Gantz, J., and Rydning, J. (2017). Data Age 2025: The Evolution of Data to Life-Critical. Don\u2019t Focus on Big Data; Focus on the Data That\u2019s Big, IDC. International Data Corporation (IDC) White Paper."},{"key":"ref_2","unstructured":"United Nations Office on Drugs and Crime (UNODC) (2022, March 01). Global Study on Homicide 2019. Data: UNODC Homicide Statistics 2019. Available online: https:\/\/www.unodc.org\/documents\/data-and-analysis\/gsh\/Booklet_5.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Namitha, K., and Narayanan, A. (2018, January 21\u201322). Video synopsis: State-of-the-art and research challenges. Proceedings of the 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET), Kottayam, India.","DOI":"10.1109\/ICCSDET.2018.8821157"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ingle, P., and Kim, Y. (2023). Video Synopsis Algorithms and Framework: A Survey and Comparative Evaluation. Systems, 11.","DOI":"10.3390\/systems11020108"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3798","DOI":"10.1109\/TIP.2018.2823420","article-title":"Video synopsis in complex situations","volume":"27","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"32331","DOI":"10.1007\/s11042-020-09493-2","article-title":"Preserving interactions among moving objects in surveillance video synopsis","volume":"79","author":"K","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8318","DOI":"10.1109\/TIP.2021.3114986","article-title":"Scene adaptive online surveillance video synopsis via dynamic tube rearrangement using octree","volume":"30","author":"Yang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s12046-022-01937-9","article-title":"PanoSyn: Immersive video synopsis for spherical surveillance video","volume":"47","author":"Priyadharshini","year":"2022","journal-title":"S\u0101dhan\u0101"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.1109\/TPAMI.2008.29","article-title":"Nonchronological video synopsis and indexing","volume":"30","author":"Pritch","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1109\/TCSVT.2014.2308603","article-title":"Maximum a posteriori probability estimation for online surveillance video synopsis","volume":"24","author":"Huang","year":"2014","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.neucom.2016.11.011","article-title":"Graph coloring based surveillance video synopsis","volume":"225","author":"He","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.1109\/TIP.2019.2942543","article-title":"Collision-free video synopsis incorporating object speed and size changes","volume":"29","author":"Nie","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3873","DOI":"10.1109\/TIP.2019.2903322","article-title":"Rearranging online tubes for streaming video synopsis: A dynamic graph coloring approach","volume":"28","author":"Ruan","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/TCE.2020.2981829","article-title":"HSAJAYA: An improved optimization scheme for consumer surveillance video synopsis generation","volume":"66","author":"Ghatak","year":"2020","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1007\/s11760-020-01794-1","article-title":"Object-based video synopsis approach using particle swarm optimization","volume":"15","author":"Moussa","year":"2021","journal-title":"Signal Image Video Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103817","DOI":"10.1016\/j.dsp.2022.103817","article-title":"An improved tube rearrangement strategy for choice-based surveillance video synopsis generation","volume":"132","author":"Ghatak","year":"2022","journal-title":"Digit. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1109\/TCSVT.2015.2430692","article-title":"Multicamera joint video synopsis","volume":"26","author":"Zhu","year":"2015","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/TCSVT.2009.2013517","article-title":"Equivalent key frames selection based on iso-content principles","volume":"19","author":"Panagiotakis","year":"2009","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1109\/76.633496","article-title":"Video visualization for compact presentation and fast browsing of pictorial content","volume":"7","author":"Yeung","year":"1997","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sun, W., and Xie, Y. (2023, January 18\u201320). Evaluation of the Geographic Video Synopsis Effect Based on Eye Movement Data. Proceedings of the 2023 4th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC), Nanjing, China.","DOI":"10.1109\/ISCEIC59030.2023.10271191"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104057","DOI":"10.1016\/j.jvcir.2024.104057","article-title":"Surveillance video synopsis framework base on tube set","volume":"98","author":"Zhang","year":"2024","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ingle, P.Y., Kim, Y., and Kim, Y.G. (2022). Dvs: A drone video synopsis towards storing and analyzing drone surveillance data in smart cities. Systems, 10.","DOI":"10.3390\/systems10050170"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ingle, P.Y., and Kim, Y.G. (2022). Real-time abnormal object detection for video surveillance in smart cities. Sensors, 22.","DOI":"10.3390\/s22103862"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106406","DOI":"10.1016\/j.engappai.2023.106406","article-title":"Multiview abnormal video synopsis in real-time","volume":"123","author":"Ingle","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Parab, M., and Ingle, P. (2024, January 2\u20133). Innovative Method for Camouflaged Wildlife Segmentation in Agricultural Practices. Proceedings of the 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT), Gharuan, India.","DOI":"10.1109\/InCACCT61598.2024.10551184"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ingle, P., and Kim, Y.G. (2024, January 8\u201312). Integrated Interoperability Based Panoramic Video Synopsis Framework. Proceedings of the 39th ACM\/SIGAPP Symposium on Applied Computing, Avila, Spain.","DOI":"10.1145\/3605098.3635937"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"163637","DOI":"10.1109\/ACCESS.2020.3020808","article-title":"Application of migration image registration algorithm based on improved SURF in remote sensing image mosaic","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/s42979-022-01608-w","article-title":"Image enhancement and exposure correction using convolutional neural network","volume":"4","author":"Parab","year":"2023","journal-title":"SN Comput. Sci."},{"key":"ref_29","first-page":"012025","article-title":"Comparison of speeded-up robust feature (SURF) and oriented FAST and rotated BRIEF (ORB) methods in identifying museum objects using Low light intensity images","volume":"Volume 537","author":"Setiawan","year":"2020","journal-title":"IOP Conference Series: Earth and Environmental Science"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bhanushali, A., Parab, M., Kumar, B.P., and Ingle, P. (2024, January 2\u20133). Adversarial Attack on 3D Fused Sensory Data in Drone Surveillance. Proceedings of the 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT), Gharuan, India.","DOI":"10.1109\/InCACCT61598.2024.10551069"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"83","DOI":"10.5194\/isprs-archives-XLII-3-W10-83-2020","article-title":"Image mosaic algorithm based on PCA-ORB feature matching","volume":"42","author":"Zhu","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"96","DOI":"10.30534\/ijatcse\/2020\/1891.12020","article-title":"Feature-based Automatic Image Stitching Using SIFT, KNN and RANSAC","volume":"9","author":"Caparas","year":"2020","journal-title":"Int. J. Adv. Trends Comput. Sci. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hoang, V., Tran, D., Nhu, N., Pham, T., and Pham, V. (2020). Deep feature extraction for panoramic image stitching. Asian Conference on Intelligent Information and Database Systems, Springer.","DOI":"10.1007\/978-3-030-42058-1_12"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, M., Niu, S., and Yang, X. (2017, January 13\u201317). A novel panoramic image stitching algorithm based on ORB. Proceedings of the 2017 International Conference on Applied System Innovation (ICASI), Sapporo, Japan.","DOI":"10.1109\/ICASI.2017.7988559"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Win, K., and Kitjaidure, Y. (2018, January 21\u201324). Biomedical images stitching using orb feature based approach. Proceedings of the 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Bangkok, Thailand.","DOI":"10.1109\/ICIIBMS.2018.8549931"},{"key":"ref_36","first-page":"1096","article-title":"Holoentropy measures for image stitching of scenes acquired under CAMERA unknown or arbitrary positions","volume":"33","author":"Delphin","year":"2021","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"032100","DOI":"10.1088\/1757-899X\/782\/3\/032100","article-title":"March. SIFT Feature Image Stitching Based on Improved Cuckoo Algorithm","volume":"Volume 782","author":"Li","year":"2020","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s11263-006-0002-3","article-title":"Automatic panoramic image stitching using invariant features","volume":"74","author":"Brown","year":"2007","journal-title":"Int. J. Comput. Vis."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zaragoza, J., Chin, T., Brown, M., and Suter, D. (2013, January 23\u201328). As-projective-as-possible image stitching with moving DLT. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.303"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"He, X., He, L., and Li, X. (2021, January 10\u201313). Image Stitching via Convolutional Neural Network. Proceedings of the 2021 7th International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/ICCC54389.2021.9674411"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Shen, C., Ji, X., and Miao, C. (2019, January 4\u20139). Real-time image stitching with convolutional neural networks. Proceedings of the 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR), Irkutsk, Russia.","DOI":"10.1109\/RCAR47638.2019.9044010"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Jia, Q., Li, Z., Fan, X., Zhao, H., Teng, S., Ye, X., and Latecki, L. (2021, January 20\u201325). Leveraging line-point consistence to preserve structures for wide parallax image stitching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01201"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Nie, L., Lin, C., Liao, K., Liu, S., and Zhao, Y. (2023, January 2\u20136). 2023, Parallax-Tolerant Unsupervised Deep Image Stitching. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00680"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1109\/TMM.2017.2777461","article-title":"Parallax-tolerant image stitching based on robust elastic warping","volume":"20","author":"Li","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Du, P., Ning, J., Cui, J., Huang, S., Wang, X., and Wang, J. (2022, January 18\u201324). Geometric structure preserving warp for natural image stitching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00367"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Jia, Y., Li, Z., Zhang, L., Song, B., and Song, R. (2024). Semantic Aware Stitching for Panorama. Sensors, 24.","DOI":"10.3390\/s24113512"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kim, D., Kim, J., Kwon, J., and Kim, T. (2019, January 14\u201319). Depth-controllable very deep super-resolution network. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8851874"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., and Tang, X. (2014, January 6\u201312). Learning a Deep Convolutional Network for Image Super Resolution. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A., Bishop, R., Rueckert, D., and Wang, Z. (2016, January 27\u201330). Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Schulter, S., Leistner, C., and Bischof, H. (2015, January 7\u201312). Fast and accurate image upscaling with super-resolution forests. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299003"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ahn, N., Kang, B., and Sohn, K. (2018, January 8\u201314). Fast, accurate, and lightweight super-resolution with cascading residual network. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_16"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Fan, Y., Shi, H., Yu, J., Liu, D., Han, W., Yu, H., Wang, Z., Wang, X., and Huang, T. (2017, January 21\u201326). Balanced two-stage residual networks for image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.154"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Lai, W., Huang, J., Ahuja, N., and Yang, M. (2017, January 21\u201326). Deep laplacian pyramid networks for fast and accurate super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.618"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image super-resolution using very deep residual channel attention networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Cheng, P., Tang, X., Liang, W., Li, Y., Cong, W., and Zang, C. (2023). Tiny-YOLOv7: Tiny Object Detection Model for Drone Imagery. International Conference on Image and Graphics, Springer.","DOI":"10.1007\/978-3-031-46311-2_5"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"113054","DOI":"10.1016\/j.eswa.2019.113054","article-title":"DNNRec: A novel deep learning based hybrid recommender system","volume":"144","author":"Kiran","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"118933","DOI":"10.1016\/j.eswa.2022.118933","article-title":"A smart decision support system to diagnose arrhythymia using ensembled ConvNet and ConvNet-LSTM model","volume":"213","author":"Tiwari","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_59","unstructured":"Ingle, P., Parab, M., Lendave, P., Bhanushali, A., and Bn, P.K. (2022, March 01). A Comprehensive Study on LLM Agent Challenges. Available online: https:\/\/aair-lab.github.io\/aia2024\/papers\/ingle_aia24.pdf."},{"key":"ref_60","unstructured":"Charbonnier, P., Blanc-Feraud, L., Aubert, G., and Barlaud, M. (1994, January 13\u201316). Two deterministic half-quadratic regularization algorithms for computed imaging. Proceedings of the 1st International Conference on Image Processing, Austin, TX, USA."},{"key":"ref_61","unstructured":"Yamanaka, J., Kuwashima, S., and Kurita, T. (2017). Fast and accurate image super resolution by deep CNN with skip connection and network in network. Neural Information Processing: 24th International Conference, ICONIP 2017, Guangzhou, China, 14\u201318 November 2017, Proceedings, Part II 24, Springer."},{"key":"ref_62","first-page":"012115","article-title":"September. A review over panoramic image stitching techniques","volume":"Volume 1999","author":"Abbadi","year":"2021","journal-title":"Journal of Physics: Conference Series"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_64","first-page":"2287","article-title":"Stereo matching by training a convolutional neural network to compare image patches","volume":"17","author":"Zbontar","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_65","unstructured":"Gu, Y., Liao, X., and Qin, X. (2022). YouTube-GDD: A challenging gun detection dataset with rich contextual information. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Shenoy, R., Yadav, D., Lakhotiya, H., and Sisodia, J. (2022, January 9\u201311). An Intelligent Framework for Crime Prediction Using Behavioural Tracking and Motion Analysis. Proceedings of the 2022 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India.","DOI":"10.1109\/ESCI53509.2022.9758281"},{"key":"ref_67","unstructured":"Kinga, D., and Adam, J. (2015, January 7\u20139). A method for stochastic optimization. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5981","DOI":"10.1007\/s10462-022-10147-y","article-title":"Video super-resolution based on deep learning: A comprehensive survey","volume":"55","author":"Liu","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"373","DOI":"10.3233\/ICA-200632","article-title":"Deep learning-based video surveillance system managed by low cost hardware and panoramic cameras","volume":"27","author":"Dominguez","year":"2020","journal-title":"Integr. Comput.-Aided Eng."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Peronikolis, M., and Panagiotakis, C. (2024). Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets. Appl. Sci., 14.","DOI":"10.20944\/preprints202404.1241.v1"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1838","DOI":"10.1109\/JPROC.2021.3117472","article-title":"Video summarization using deep neural networks: A survey","volume":"109","author":"Apostolidis","year":"2021","journal-title":"Proc. IEEE"}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/2\/110\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:30:56Z","timestamp":1760027456000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/2\/110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,11]]},"references-count":71,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["systems13020110"],"URL":"https:\/\/doi.org\/10.3390\/systems13020110","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,11]]}}}