{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T06:57:02Z","timestamp":1764053822199,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:00:00Z","timestamp":1675036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Oceans and Fisheries","award":["G22202202102201","K_G012002236201"],"award-info":[{"award-number":["G22202202102201","K_G012002236201"]}]},{"DOI":"10.13039\/501100002631","name":"Korea Agency for Technology and Standards in 2022","doi-asserted-by":"publisher","award":["G22202202102201","K_G012002236201"],"award-info":[{"award-number":["G22202202102201","K_G012002236201"]}],"id":[{"id":"10.13039\/501100002631","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>During the last decade, surveillance cameras have spread quickly; their spread is predicted to increase rapidly in the following years. Therefore, browsing and analyzing these vast amounts of created surveillance videos effectively is vital in surveillance applications. Recently, a video synopsis approach was proposed to reduce the surveillance video duration by rearranging the objects to present them in a portion of time. However, performing a synopsis for all the persons in the video is not efficacious for crowded videos. Different clustering and user-defined query methods are introduced to generate the video synopsis according to general descriptions such as color, size, class, and motion. This work presents a user-defined query synopsis video based on motion descriptions and specific visual appearance features such as gender, age, carrying something, having a baby buggy, and upper and lower clothing color. The proposed method assists the camera monitor in retrieving people who meet certain appearance constraints and people who enter a predefined area or move in a specific direction to generate the video, including a suspected person with specific features. After retrieving the persons, a whale optimization algorithm is applied to arrange these persons reserving chronological order, reducing collisions, and assuring a short synopsis video. The evaluation of the proposed work for the retrieval process in terms of precision, recall, and F1 score ranges from 83% to 100%, while for the video synopsis process, the synopsis video length compared to the original video is decreased by 68% to 93.2%, and the interacting tube pairs are preserved in the synopsis video by 78.6% to 100%.<\/jats:p>","DOI":"10.3390\/s23031521","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T04:31:27Z","timestamp":1675053087000},"page":"1521","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["User Preference-Based Video Synopsis Using Person Appearance and Motion Descriptions"],"prefix":"10.3390","volume":"23","author":[{"given":"Rasha","family":"Shoitan","sequence":"first","affiliation":[{"name":"Computer and Systems Department, Electronics Research Institute (ERI), Cairo 11843, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3647-1993","authenticated-orcid":false,"given":"Mona M.","family":"Moussa","sequence":"additional","affiliation":[{"name":"Computer and Systems Department, Electronics Research Institute (ERI), Cairo 11843, Egypt"}]},{"given":"Sawsan Morkos","family":"Gharghory","sequence":"additional","affiliation":[{"name":"Computer and Systems Department, Electronics Research Institute (ERI), Cairo 11843, Egypt"}]},{"given":"Heba A.","family":"Elnemr","sequence":"additional","affiliation":[{"name":"Computer and Systems Department, Electronics Research Institute (ERI), Cairo 11843, Egypt"},{"name":"Faculty of Computer and Software Engineering, Misr University for Science and Technology, 6th of October City 12566, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0184-7599","authenticated-orcid":false,"given":"Young-Im","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam 13415, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7351-625X","authenticated-orcid":false,"given":"Mohamed S.","family":"Abdallah","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam 13415, Republic of Korea"},{"name":"Informatics Department, Electronics Research Institute (ERI), Cairo 11843, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1438","DOI":"10.1109\/TPAMI.2020.2983929","article-title":"A Sparse Sampling-Based Framework for Semantic Fast-Forward of First-Person Videos","volume":"43","author":"Silva","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Besl, P.J. (1988). Surfaces in Range Image Understanding, Springer.","DOI":"10.1007\/978-1-4612-3906-2"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.neucom.2019.07.108","article-title":"Video summarization via block sparse dictionary selection","volume":"378","author":"Ma","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TIP.2015.2507942","article-title":"Surveillance video synopsis via scaling down objects","volume":"25","author":"Li","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.neucom.2016.11.011","article-title":"Neurocomputing Graph coloring based surveillance video synopsis","volume":"225","author":"He","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/LSP.2016.2633374","article-title":"Fast Online Video Synopsis Based on Potential Collision Graph","volume":"24","author":"He","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1109\/TVCG.2012.176","article-title":"Compact video synopsis via global spatiotemporal optimization","volume":"19","author":"Nie","year":"2013","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1007\/s00500-015-1823-1","article-title":"An optimized video synopsis algorithm and its distributed processing model","volume":"21","author":"Lin","year":"2017","journal-title":"Soft Comput."},{"key":"ref_9","first-page":"V","article-title":"Surveillance Video Synopsis While Preserving Object Motion Structure and Interaction","volume":"Volume 460","author":"Raman","year":"2016","journal-title":"Proceedings of the International Conference on Computer Vision and Image Processing"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4429","DOI":"10.1007\/s11042-019-7389-7","article-title":"An improved surveillance video synopsis framework: A HSATLBO optimization approach","volume":"79","author":"Ghatak","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_11","unstructured":"Ghatak, S., and Rup, S. (2020). Information, Photonics and Communication, Springer."},{"key":"ref_12","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_13","unstructured":"Yao, T., Xiao, M., Ma, C., Shen, C., and Li, P. (2014, January 29\u201330). Object based video synopsis. Proceedings of the 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), Ottawa, ON, Canada."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1007\/s12652-015-0278-7","article-title":"Optimization method for trajectory combination in surveillance video synopsis based on genetic algorithm","volume":"6","author":"Xu","year":"2015","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"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":"2020","journal-title":"Signal Image Video Process."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Feng, S., Liao, S., Yuan, Z., and Li, S.Z. (2010, January 23\u201326). Online principal background selection for video synopsis. Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.13"},{"key":"ref_18","first-page":"41","article-title":"Improved Adaptive Background Subtraction Method Using Pixel-based Segmenter","volume":"2703","author":"Baskurt","year":"2017","journal-title":"Comput. Sci. Res. Notes"},{"key":"ref_19","unstructured":"Feng, S., Lei, Z., Yi, D., and Li, S.Z. (2012, January 16\u201321). Online content-aware video condensation. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2246","DOI":"10.3906\/elk-1709-245","article-title":"Long-term multiobject tracking using alternative correlation filters","volume":"26","author":"Samet","year":"2018","journal-title":"Turkish J. Electr. Eng. Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lu, M., Wang, Y., and Pan, G. (2013, January 26\u201331). Generating fluent tubes in video synopsis. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638063"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9885","DOI":"10.1007\/s11042-015-2714-2","article-title":"Low-complexity range tree for video synopsis system","volume":"75","author":"Hsia","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102988","DOI":"10.1016\/j.dsp.2021.102988","article-title":"GAN based efficient foreground extraction and HGWOSA based optimization for video synopsis generation","volume":"111","author":"Ghatak","year":"2021","journal-title":"Digit. Signal Process. A Rev. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.neucom.2014.12.044","article-title":"Summarizing surveillance videos with local-patch-learning-based abnormality detection, blob sequence optimization, and type-based synopsis","volume":"155","author":"Lin","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_25","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":"Namitha","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Pritch, Y., Ratovitch, S., Hendel, A., and Peleg, S. (2009, January 2\u20134). Clustered synopsis of surveillance video. Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, Genova, Italy.","DOI":"10.1109\/AVSS.2009.53"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3457","DOI":"10.1109\/TITS.2019.2929618","article-title":"Query-Based Video Synopsis for Intelligent Traffic Monitoring Applications","volume":"21","author":"Ahmed","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3954","DOI":"10.1007\/s10489-021-02636-4","article-title":"Interactive visualization-based surveillance video synopsis","volume":"52","author":"Namitha","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The Whale Optimization Algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., and Wang, X. (2021). ByteTrack: Multi-Object Tracking by Associating Every Detection Box. arXiv.","DOI":"10.1007\/978-3-031-20047-2_1"},{"key":"ref_32","first-page":"720","article-title":"Multi-Vehicle Tracking Using Heterogeneous Neural Networks for Appearance and Motion Features","volume":"20","author":"Abdallah","year":"2022","journal-title":"Int. J. Intell. Transp. Syst. Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tang, C., Sheng, L., Zhang, Z., and Hu, X. (November, January 27). Improving pedestrian attribute recognition with weakly-supervised multi-scale attribute-specific localization. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00510"},{"key":"ref_34","unstructured":"Jaderberg, M. (2015, January 7\u201312). Spatial Transformer Networks. Proceedings of the NIPS\u201915: Proceedings of the 28th International Conference on Neural Information Processing Systems, Cambridge, MA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Deng, Y., Luo, P., Loy, C., and Tang, X. (2014, January 3\u20137). Pedestrian Attribute Recognition at Far Distance. Proceedings of the MM\u201914: Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654966"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Benfold, B., and Reid, I. (2011, January 20\u201325). Stable multi-target tracking in real-time surveillance video. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995667"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.cviu.2019.02.004","article-title":"Video synopsis: A survey","volume":"181","author":"Baskurt","year":"2019","journal-title":"Comput. Vis. Image Underst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1521\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:19:49Z","timestamp":1760120389000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1521"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,30]]},"references-count":37,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031521"],"URL":"https:\/\/doi.org\/10.3390\/s23031521","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,1,30]]}}}