{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:57:22Z","timestamp":1774454242457,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T00:00:00Z","timestamp":1691452800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T00:00:00Z","timestamp":1691452800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976032"],"award-info":[{"award-number":["61976032"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010868","name":"Ministry of Public Security of the People's Republic of China","doi-asserted-by":"publisher","award":["2020YYCXHNST046"],"award-info":[{"award-number":["2020YYCXHNST046"]}],"id":[{"id":"10.13039\/501100010868","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16386-7","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T06:01:46Z","timestamp":1691474506000},"page":"21905-21928","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Key frame extraction method with global information balance"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0206-2622","authenticated-orcid":false,"given":"Xiaohu","family":"Shen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jubai","family":"An","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhisong","family":"Teng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,8]]},"reference":[{"key":"16386_CR1","doi-asserted-by":"publisher","unstructured":"Ahmad F, Li N, Tahir M (2019) An improved D-CNN based on YOLOv3 for pedestrian detection. In: IEEE 4th international conference on signal and image processing (ICSIP), Wuxi, pp 405\u2013409. https:\/\/doi.org\/10.1109\/SIPROCESS.2019.8868839","DOI":"10.1109\/SIPROCESS.2019.8868839"},{"key":"16386_CR2","doi-asserted-by":"publisher","unstructured":"Chamasemani FF, Affendey LS, Mustapha N, Khalid F (2015) A study on surveillance video abstraction techniques, International conference on control system, computing and engineering (ICCSCE), Penang, pp 470\u2013475. https:\/\/doi.org\/10.1109\/ICCSCE.2015.7482231","DOI":"10.1109\/ICCSCE.2015.7482231"},{"issue":"11","key":"16386_CR3","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1109\/TCSVT.2010.2087491","volume":"20","author":"G Chao","year":"2010","unstructured":"Chao G, Tsai Y, Jeng S (2010) Augmented 3-D keyframe extraction for surveillance videos. IEEE Trans Circuits Syst 20(11):1395\u20131408. https:\/\/doi.org\/10.1109\/TCSVT.2010.2087491","journal-title":"IEEE Trans Circuits Syst"},{"key":"16386_CR4","doi-asserted-by":"publisher","unstructured":"Damnjanovic U, Fernandez V, Izquierdo E, Martinez JM (2008) Event detection and clustering for surveillance video summarization. In: 2008 ninth international workshop on image analysis for multimedia interactive services, Klagenfurt, pp 63\u201366. https:\/\/doi.org\/10.1109\/WIAMIS.2008.53","DOI":"10.1109\/WIAMIS.2008.53"},{"issue":"10","key":"16386_CR5","doi-asserted-by":"publisher","first-page":"961","DOI":"10.24507\/icicel.14.10.961","volume":"14","author":"L Dang","year":"2020","unstructured":"Dang L, Nguyen GT, Cao T (2020) Object tracking using improved deep_sort_YOLOv3 architecture. ICIC Express Lett 14(10):961\u2013969. https:\/\/doi.org\/10.24507\/icicel.14.10.961","journal-title":"ICIC Express Lett"},{"key":"16386_CR6","doi-asserted-by":"publisher","unstructured":"Donahue J, Hendricks LA, Rohrbach M, Venugopalan S, Guadarrama S, Saenko K, Darrell T (2017) Long-term recurrent convolutional networks for visual recognition and description. In: Computer vision and pattern recognition, Hawaii, pp 677\u2013691. https:\/\/doi.org\/10.1590\/S1676-06032009000300007","DOI":"10.1590\/S1676-06032009000300007"},{"issue":"5","key":"16386_CR7","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1002\/spe.2742","volume":"50","author":"CR Dow","year":"2020","unstructured":"Dow CR, Ngo HH, Lee LH, Lai PY, Wang KC, Bui VT (2020) A crosswalk pedestrian recognition system by using deep learning and zebra crossing recognition techniques. Softw Pract Exper 50(5):630\u2013644. https:\/\/doi.org\/10.1002\/spe.2742","journal-title":"Softw Pract Exper"},{"issue":"06","key":"16386_CR8","first-page":"790","volume":"34","author":"R Ge","year":"2017","unstructured":"Ge R, Wang ZH, Xu X, Ji Y, Liu C, Gong SR (2017) Action recognition with hierarchical convolutional neural networks features and bidirectional long short-term memory model. Control Theory Appl 34(06):790\u2013796","journal-title":"Control Theory Appl"},{"key":"16386_CR9","doi-asserted-by":"publisher","unstructured":"Ji Z, Su Y, Qian R, Ma J (2010) Surveillance video summarization based on moving object detection and trajectory extraction. In: 2010 2nd international conference on signal processing systems, Dalian, CN, pp V2-250\u2013V2-253. https:\/\/doi.org\/10.1109\/ICSPS.2010.5555504","DOI":"10.1109\/ICSPS.2010.5555504"},{"issue":"9\u201310","key":"16386_CR10","doi-asserted-by":"publisher","first-page":"3672","DOI":"10.1080\/01431161.2018.1552812","volume":"40","author":"J Ji","year":"2019","unstructured":"Ji J, Yao Y, Wei J, Quan Y (2019) Perceptual hashing for SAR image segmentation. Int J Remote Sens 40(9\u201310):3672\u20133688","journal-title":"Int J Remote Sens"},{"key":"16386_CR11","doi-asserted-by":"publisher","unstructured":"Kajabad EN, Ivanov SV, Ramezanzade N (2020) Customer detection and tracking by deep learning and Kalman filter algorithms. In: 2020 international conference on electrical, communication, and computer engineering (ICECCE), Istanbul, pp 1\u20136. https:\/\/doi.org\/10.1109\/ICECCE49384.2020.9179224","DOI":"10.1109\/ICECCE49384.2020.9179224"},{"key":"16386_CR12","doi-asserted-by":"publisher","unstructured":"Kar A, Rai N, Sikka K, Sharma G (2017) AdaScan: adaptive scan pooling in deep convolutional neural networks for human action recognition in videos. In: IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, pp 5699\u20135708. https:\/\/doi.org\/10.1109\/CVPR.2017.604","DOI":"10.1109\/CVPR.2017.604"},{"issue":"01","key":"16386_CR13","first-page":"129","volume":"35","author":"Z Lan","year":"2016","unstructured":"Lan Z, Shuai D, Li YC (2016) An algorithm of key frame extraction in road monitoring video based on correlation coefficient. J Chongqing Jiaotong Univ (Nat Sci) 35(01):129\u2013133+176","journal-title":"J Chongqing Jiaotong Univ (Nat Sci)"},{"key":"16386_CR14","doi-asserted-by":"publisher","unstructured":"Laroca R, Severo E, Zanlorensi LA, Oliveira LS, Goncalves GR, Schwartz WR, Menotti D (2018) A robust real-time automatic license plate recognition based on the YOLO detector. In: Proc. International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, pp 1\u201310. https:\/\/doi.org\/10.1109\/IJCNN.2018.8489629","DOI":"10.1109\/IJCNN.2018.8489629"},{"key":"16386_CR15","doi-asserted-by":"publisher","first-page":"2395","DOI":"10.1109\/TIP.2019.2948286","volume":"29","author":"S Lee","year":"2020","unstructured":"Lee S, Kim HG, Ro YM (2020) BMAN: bidirectional multi-scale aggregation networks for abnormal event detection. IEEE Trans Image Process 29:2395\u20132408. https:\/\/doi.org\/10.1109\/TIP.2019.2948286","journal-title":"IEEE Trans Image Process"},{"key":"16386_CR16","doi-asserted-by":"publisher","unstructured":"Li Y, Lan C, Xing J, Zeng W, Yuan C, Liu J (2016) Online human action detection using joint classification-regression recurrent neural networks. In: European conference on computer vision, Amsterdam, pp 203\u2013220. https:\/\/doi.org\/10.1007\/978-3-319-46478-7_13","DOI":"10.1007\/978-3-319-46478-7_13"},{"key":"16386_CR17","doi-asserted-by":"publisher","unstructured":"Lin T, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Computer vision and pattern recognition, Hawaii, pp 936\u2013944. https:\/\/doi.org\/10.1109\/CVPR.2017.106","DOI":"10.1109\/CVPR.2017.106"},{"issue":"08","key":"16386_CR18","first-page":"933","volume":"18","author":"Y Liu","year":"2013","unstructured":"Liu Y, Zhang S, Wang R, Zhang Y (2013) Key frame extraction based on the visual attention model for lane surveillance video. J Image Graph 18(08):933\u2013943","journal-title":"J Image Graph"},{"issue":"2","key":"16386_CR19","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1134\/S1054661818020190","volume":"28","author":"Y Luo","year":"2018","unstructured":"Luo Y, Zhou H, Qin T, Chen X, Yun M (2018) Key frame extraction of surveillance video based on moving object detection and image similarity. Pattern Recogn Img Anal 28(2):225\u2013231. https:\/\/doi.org\/10.1134\/S1054661818020190","journal-title":"Pattern Recogn Img Anal"},{"key":"16386_CR20","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-642-12900-1_3","volume-title":"Studies in computational intelligence","author":"X Ma","year":"2010","unstructured":"Ma X, Chen X, Khokhar A, Dan S (2010) Motion trajectory-based video retrieval, classification, and summarization. In: Studies in computational intelligence. Springer, Berlin, Heidelberg, pp 53\u201382. https:\/\/doi.org\/10.1007\/978-3-642-12900-1_3"},{"key":"16386_CR21","doi-asserted-by":"publisher","unstructured":"Mahmoud KM, Ismail MA, Ghanem NM (2013) Vscan: an enhanced video summarization using density-based spatial clustering. In: International conference on image analysis and processing, Naples, pp 733\u2013742. https:\/\/doi.org\/10.1007\/978-3-642-41181-6_74","DOI":"10.1007\/978-3-642-41181-6_74"},{"key":"16386_CR22","unstructured":"Ministry of Housing and Urban-Rural Development of the People\u2019s Republic of China. (2011) GB50688\u20132011, code for design of urban road traffic facility. https:\/\/www.gov.cn\/zhengce\/zhengceku\/2019-08\/20\/content_5454346.htm. Accessed 20 Aug\u00a02019"},{"key":"16386_CR23","doi-asserted-by":"publisher","first-page":"280","DOI":"10.18653\/v1\/K16-1028","volume-title":"Proceedings of the 20th SIGNLL conference on computational natural language learning","author":"R Nallapati","year":"2016","unstructured":"Nallapati R, Zhou B, Santos C, Gulcehre C (2016) Abstractive text summarization using sequence-to-sequence RNNs and beyond. In: Proceedings of the 20th SIGNLL conference on computational natural language learning, pp 280\u2013290. https:\/\/doi.org\/10.18653\/v1\/K16-1028"},{"key":"16386_CR24","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1109\/ICCCT.2014.7001508","volume-title":"2014 international conference on computer and communication technology (ICCCT)","author":"SC Raikwar","year":"2014","unstructured":"Raikwar SC, Bhatnagar C, Jalal AS (2014) A framework for key frame extraction from surveillance video. In: 2014 international conference on computer and communication technology (ICCCT). Allahabad, pp 297\u2013300. https:\/\/doi.org\/10.1109\/ICCCT.2014.7001508"},{"issue":"8","key":"16386_CR25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0135694","volume":"10","author":"Z Ran","year":"2015","unstructured":"Ran Z, Yao CW, Jin H, Zhu L, Zhang Q, Deng W (2015) Parallel key frame extraction for surveillance video Service in a Smart City. PLoS One 10(8):e0135694","journal-title":"PLoS One"},{"key":"16386_CR26","doi-asserted-by":"publisher","unstructured":"Sang D, Hung D (2019) YOLOv3-VD: a sparse network for vehicle detection using variational dropout. In: Proc. symposium on information and communication technology (SoICT), Guangzhou, pp 280\u2013284. https:\/\/doi.org\/10.1145\/3368926.3369691","DOI":"10.1145\/3368926.3369691"},{"issue":"9","key":"16386_CR27","doi-asserted-by":"publisher","first-page":"5241","DOI":"10.1002\/int.22511","volume":"36","author":"XH Shen","year":"2021","unstructured":"Shen XH, An JB, Teng ZS (2021) Recognition method of traffic violations based on complex interaction between multiple entities. Int J Intell Syst 36(9):5241\u20135263. https:\/\/doi.org\/10.1002\/int.22511","journal-title":"Int J Intell Syst"},{"key":"16386_CR28","doi-asserted-by":"publisher","unstructured":"Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. In: Neural information processing systems, Montreal, pp 568\u2013576. https:\/\/doi.org\/10.1002\/14651858.CD001941.pub3","DOI":"10.1002\/14651858.CD001941.pub3"},{"issue":"Apr 26","key":"16386_CR29","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.neucom.2015.07.131","volume":"187","author":"X Song","year":"2016","unstructured":"Song X, Sun L, Lei J, Tao D, Yuan G, Song M (2016) Event-based large scale surveillance video summarization. Neuro Comput 187(Apr 26):66\u201374. https:\/\/doi.org\/10.1016\/j.neucom.2015.07.131","journal-title":"Neuro Comput"},{"key":"16386_CR30","doi-asserted-by":"publisher","unstructured":"Venugopalan S, Rohrbach M, Donahue J, Mooney R, Darrell T, Saenko K (2015) Sequence to sequence -- video to text. In: 2015 IEEE international conference on computer vision (ICCV), Santiago, pp 4534\u20134542. https:\/\/doi.org\/10.1109\/ICCV.2015.515","DOI":"10.1109\/ICCV.2015.515"},{"key":"16386_CR31","doi-asserted-by":"publisher","unstructured":"Womg A, Shafiee MJ, Li F, Chwyl B (2018) Tiny SSD: a tiny single-shot detection deep convolutional neural network for real-time embedded object detection. In: 15th conference on computer and robot vision (CRV), Toronto, pp 95\u2013101. https:\/\/doi.org\/10.1109\/CRV.2018.00023","DOI":"10.1109\/CRV.2018.00023"},{"issue":"02","key":"16386_CR32","first-page":"1\u20135+11","volume":"30","author":"J Xia","year":"2010","unstructured":"Xia J, Wang J, Chen JM, Cui Z (2010) Key frame extraction of traffic video based on virtual detection line. J Suzhou Univ 30(02):1\u20135+11","journal-title":"J Suzhou Univ"},{"key":"16386_CR33","doi-asserted-by":"publisher","unstructured":"Xiao T, Xu Y, Yang K, Zhang J, Peng Y, Zhang Z (2015) The application of two-level attention models in deep convolutional neural network for fine-grained image classification. In: Computer vision and pattern recognition, Boston, pp 842\u2013850. https:\/\/doi.org\/10.1109\/ICCV.2015.515","DOI":"10.1109\/ICCV.2015.515"},{"key":"16386_CR34","doi-asserted-by":"publisher","unstructured":"Yan X, Gilani SZ, Qin H, Feng M, Zhang L, Mian A (2018) Deep keyframe detection in human action videos. arXiv preprint. https:\/\/doi.org\/10.48550\/arXiv.1804.10021","DOI":"10.48550\/arXiv.1804.10021"},{"key":"16386_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2020.102756","volume":"102","author":"YH Yin","year":"2020","unstructured":"Yin YH, Li HF, Fu W (2020) Faster-YOLO: an accurate and faster object detection method. Sci Signal Process 102:102756. https:\/\/doi.org\/10.1016\/j.dsp.2020.102756","journal-title":"Sci Signal Process"},{"key":"16386_CR36","doi-asserted-by":"publisher","unstructured":"Zhang J, Chen X, Li Y, Chen Y, Mou L (2021) Pedestrian detection algorithm based on improved Yolo v3. In: 2021 IEEE international conference on power, intelligent computing and systems (ICPICS), Shenyang, pp 180\u2013183. https:\/\/doi.org\/10.1109\/ICPICS52425.2021.9524267","DOI":"10.1109\/ICPICS52425.2021.9524267"},{"issue":"03","key":"16386_CR37","first-page":"311","volume":"30","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Zhang J, Tao R (2021) Key frame extraction of surveillance video based on fractional Fourier transform. J Beijing Inst Technol 30(03):311\u2013321","journal-title":"J Beijing Inst Technol"},{"issue":"06","key":"16386_CR38","first-page":"164","volume":"29","author":"MJ Zhong","year":"2019","unstructured":"Zhong MJ, Zhang YB (2019) A key frame extraction method of vehicle surveillance video based on visual saliency. Comput Technol Dev 29(06):164\u2013169","journal-title":"Comput Technol Dev"},{"key":"16386_CR39","doi-asserted-by":"publisher","first-page":"174424","DOI":"10.1109\/ACCESS.2020.3025774","volume":"8","author":"Q Zhong","year":"2020","unstructured":"Zhong Q, Zhang Y, Zhang J (2020) Key frame extraction algorithm of motion video based on priori. IEEE Access 8:174424\u2013174436","journal-title":"IEEE Access"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16386-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16386-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16386-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T13:23:31Z","timestamp":1708608211000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16386-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,8]]},"references-count":39,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["16386"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16386-7","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,8]]},"assertion":[{"value":"28 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Written informed consent for the publication of this paper was obtained from all authors.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}