{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T01:26:34Z","timestamp":1771809994132,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002628","name":"Incheon National University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002628","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s11554-024-01531-8","type":"journal-article","created":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T03:43:43Z","timestamp":1723520623000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["TinyCount: an efficient crowd counting network for intelligent surveillance"],"prefix":"10.1007","volume":"21","author":[{"given":"Hyeonbeen","family":"Lee","sequence":"first","affiliation":[]},{"given":"Jangho","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"issue":"1","key":"1531_CR1","first-page":"107","volume":"3","author":"P Naharwal","year":"2023","unstructured":"Naharwal, P., et al.: Smart surveillance: a review and survey through deep learning techniques for detection & analysis. J. Sens. Netw. Data Commun. 3(1), 107\u2013116 (2023)","journal-title":"J. Sens. Netw. Data Commun."},{"key":"1531_CR2","doi-asserted-by":"crossref","unstructured":"Wang, M., Wang, X.: Automatic adaptation of a generic pedestrian detector to a specific traffic scene. In: CVPR 2011, pp.\u00a03401\u20133408. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995698"},{"issue":"18","key":"1531_CR3","doi-asserted-by":"publisher","first-page":"25443","DOI":"10.1007\/s11042-022-12370-9","volume":"81","author":"A Gomaa","year":"2022","unstructured":"Gomaa, A., Minematsu, T., Abdelwahab, M.M., Abo-Zahhad, M., Taniguchi, R.-I.: Faster cnn-based vehicle detection and counting strategy for fixed camera scenes. Multimed. Tools Appl. 81(18), 25443\u201325471 (2022)","journal-title":"Multimed. Tools Appl."},{"key":"1531_CR4","doi-asserted-by":"crossref","unstructured":"Gong, S., Loy, C.C., Xiang, T.: Security and surveillance. Visual Analysis of Humans: Looking at people, pp. 455\u2013472 (2011)","DOI":"10.1007\/978-0-85729-997-0_23"},{"key":"1531_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.patrec.2017.07.007","volume":"107","author":"VA Sindagi","year":"2018","unstructured":"Sindagi, V.A., Patel, V.M.: A survey of recent advances in cnn-based single image crowd counting and density estimation. Pattern Recognit. Lett. 107, 3\u201316 (2018)","journal-title":"Pattern Recognit. Lett."},{"key":"1531_CR6","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1007\/s10044-021-00959-z","volume":"24","author":"B Li","year":"2021","unstructured":"Li, B., Huang, H., Zhang, A., Liu, P., Liu, C.: Approaches on crowd counting and density estimation: a review. Pattern Anal. Appl. 24, 853\u2013874 (2021)","journal-title":"Pattern Anal. Appl."},{"key":"1531_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2022.104597","volume":"129","author":"MA Khan","year":"2023","unstructured":"Khan, M.A., Menouar, H., Hamila, R.: Revisiting crowd counting: State-of-the-art, trends, and future perspectives. Image Vis. Comput. 129, 104597 (2023)","journal-title":"Image Vis. Comput."},{"key":"1531_CR8","doi-asserted-by":"crossref","unstructured":"Peng, T., Li, Q., Zhu, P.: Rgb-t crowd counting from drone: a benchmark and mmccn network. In: Proceedings of the Asian Conference on Computer Vision, pp. 497\u2013513 (2020)","DOI":"10.1007\/978-3-030-69544-6_30"},{"key":"1531_CR9","doi-asserted-by":"crossref","unstructured":"Liu, Z., He, Z., Wang, L., Wang, W., Yuan, Y., Zhang, D., Zhang, J., Zhu, P., Van Gool, L., Han, J., et al.: Visdrone-cc2021: the vision meets drone crowd counting challenge results. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2830\u20132838 (2021)","DOI":"10.1109\/ICCVW54120.2021.00317"},{"key":"1531_CR10","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"1531_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 589\u2013597 (2016)","DOI":"10.1109\/CVPR.2016.70"},{"issue":"1","key":"1531_CR12","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s43762-023-00097-8","volume":"3","author":"BR Ardabili","year":"2023","unstructured":"Ardabili, B.R., Pazho, A.D., Noghre, G.A., Neff, C., Bhaskararayuni, S.D., Ravindran, A., Reid, S., Tabkhi, H.: Understanding policy and technical aspects of ai-enabled smart video surveillance to address public safety. Comput. Urban Sci. 3(1), 21 (2023)","journal-title":"Comput. Urban Sci."},{"key":"1531_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2022.103982","volume":"148","author":"HF Yang","year":"2023","unstructured":"Yang, H.F., Cai, J., Liu, C., Ke, R., Wang, Y.: Cooperative multi-camera vehicle tracking and traffic surveillance with edge artificial intelligence and representation learning. Transp. Res. Part C Emerg. Technol. 148, 103982 (2023)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"issue":"6","key":"1531_CR14","doi-asserted-by":"publisher","first-page":"7171","DOI":"10.1007\/s40747-023-01117-0","volume":"9","author":"Z Hu","year":"2023","unstructured":"Hu, Z., Lam, W.H., Wong, S., Chow, A.H., Ma, W.: Turning traffic surveillance cameras into intelligent sensors for traffic density estimation. Complex Intell. Syst. 9(6), 7171\u20137195 (2023)","journal-title":"Complex Intell. Syst."},{"issue":"3","key":"1531_CR15","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1007\/s11269-023-03714-7","volume":"38","author":"SH Kwon","year":"2024","unstructured":"Kwon, S.H., Lee, S.: Deep learning to recognize water level for agriculture reservoir using cctv imagery. Water Resour. Manag. 38(3), 1165\u20131180 (2024)","journal-title":"Water Resour. Manag."},{"key":"1531_CR16","doi-asserted-by":"publisher","first-page":"66061","DOI":"10.1109\/ACCESS.2022.3184707","volume":"10","author":"M Park","year":"2022","unstructured":"Park, M., Jeon, Y., Bak, J., Park, S., et al.: Forest-fire response system using deep-learning-based approaches with cctv images and weather data. IEEE Access 10, 66061\u201366071 (2022)","journal-title":"IEEE Access"},{"key":"1531_CR17","doi-asserted-by":"crossref","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol.\u00a01, pp.\u00a0886\u2013893. IEEE (2005)","DOI":"10.1109\/CVPR.2005.177"},{"key":"1531_CR18","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","volume":"57","author":"P Viola","year":"2004","unstructured":"Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57, 137\u2013154 (2004)","journal-title":"Int. J. Comput. Vis."},{"key":"1531_CR19","doi-asserted-by":"publisher","first-page":"9315","DOI":"10.1007\/s11042-016-3344-z","volume":"75","author":"C Gao","year":"2016","unstructured":"Gao, C., Liu, J., Feng, Q., Lv, J.: People-flow counting in complex environments by combining depth and color information. Multimed. Tools Appl. 75, 9315\u20139331 (2016)","journal-title":"Multimed. Tools Appl."},{"key":"1531_CR20","doi-asserted-by":"crossref","unstructured":"Viola and Snow, Detecting pedestrians using patterns of motion and appearance. In: Proceedings Ninth IEEE International Conference on Computer Vision, pp.\u00a0734\u2013741. IEEE (2003)","DOI":"10.1109\/ICCV.2003.1238422"},{"issue":"11","key":"1531_CR21","doi-asserted-by":"publisher","first-page":"2188","DOI":"10.1109\/TPAMI.2011.70","volume":"33","author":"J Gall","year":"2011","unstructured":"Gall, J., Yao, A., Razavi, N., Van Gool, L., Lempitsky, V.: Hough forests for object detection, tracking, and action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 33(11), 2188\u20132202 (2011)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"1531_CR22","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2009","unstructured":"Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627\u20131645 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"1531_CR23","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1109\/3468.983420","volume":"31","author":"S-F Lin","year":"2001","unstructured":"Lin, S.-F., Chen, J.-Y., Chao, H.-X.: Estimation of number of people in crowded scenes using perspective transformation. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 31(6), 645\u2013654 (2001)","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"1531_CR24","unstructured":"Lempitsky, V., Zisserman, A.: Learning to count objects in images. Advances in Neural Information Processing Systems, pp. 1324-1332, 2010."},{"key":"1531_CR25","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"1531_CR26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1531_CR27","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: alexnet-level accuracy with 50x fewer parameters and$$<$$ 0.5 mb model size. arXiv preprint arXiv:1602.07360 (2016)"},{"key":"1531_CR28","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"1531_CR29","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al.: Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"1531_CR30","unstructured":"Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning, pp.\u00a06105\u20136114. PMLR (2019)"},{"key":"1531_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848\u20136856 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"1531_CR32","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)"},{"key":"1531_CR33","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"1531_CR34","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1531_CR35","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, X., Chen, D.: Csrnet: dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1091\u20131100 (2018)","DOI":"10.1109\/CVPR.2018.00120"},{"key":"1531_CR36","unstructured":"Gao, J., Lin, W., Zhao, B., Wang, D., Gao, C., Wen, J.: C$$^{3}$$ framework: An open-source pytorch code for crowd counting. arXiv preprint arXiv:1907.02724 (2019)"},{"key":"1531_CR37","doi-asserted-by":"crossref","unstructured":"Idrees, H., Tayyab, M., Athrey, K., Zhang, D., Al-Maadeed, S., Rajpoot, N., Shah, M.: Composition loss for counting, density map estimation and localization in dense crowds. In: Proceedings of the European Conference on Computer Vision, pp. 532\u2013546 (2018)","DOI":"10.1007\/978-3-030-01216-8_33"},{"key":"1531_CR38","unstructured":"Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 833\u2013841 (2015)"},{"key":"1531_CR39","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp.\u00a0448\u2013456. PMLR (2015)"},{"key":"1531_CR40","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"1531_CR41","doi-asserted-by":"crossref","unstructured":"Cao, X., Wang, Z., Zhao, Y., Su, F.: Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision, pp. 734\u2013750 (2018)","DOI":"10.1007\/978-3-030-01228-1_45"},{"issue":"10","key":"1531_CR42","doi-asserted-by":"publisher","first-page":"3486","DOI":"10.1109\/TCSVT.2019.2919139","volume":"30","author":"J Gao","year":"2019","unstructured":"Gao, J., Wang, Q., Li, X.: Pcc net: perspective crowd counting via spatial convolutional network. IEEE Trans. Circuits Syst. Video Technol. 30(10), 3486\u20133498 (2019)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1531_CR43","doi-asserted-by":"crossref","unstructured":"Shi, X., Li, X., Wu, C., Kong, S., Yang, J., He, L.: A real-time deep network for crowd counting. In: International Conference on Acoustics, Speech and Signal Processing, pp.\u00a02328\u20132332. IEEE (2020)","DOI":"10.1109\/ICASSP40776.2020.9053780"},{"key":"1531_CR44","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.neucom.2020.05.056","volume":"407","author":"P Wang","year":"2020","unstructured":"Wang, P., Gao, C., Wang, Y., Li, H., Gao, Y.: Mobilecount: an efficient encoder-decoder framework for real-time crowd counting. Neurocomputing 407, 292\u2013299 (2020)","journal-title":"Neurocomputing"},{"key":"1531_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116662","volume":"197","author":"G Jiang","year":"2022","unstructured":"Jiang, G., Wu, R., Huo, Z., Zhao, C., Luo, J.: Ligmsanet: lightweight multi-scale adaptive convolutional neural network for dense crowd counting. Expert Syst. Appl. 197, 116662 (2022)","journal-title":"Expert Syst. Appl."},{"key":"1531_CR46","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neucom.2021.11.099","volume":"472","author":"F Zhu","year":"2022","unstructured":"Zhu, F., Yan, H., Chen, X., Li, T.: Real-time crowd counting via lightweight scale-aware network. Neurocomputing 472, 54\u201367 (2022)","journal-title":"Neurocomputing"},{"key":"1531_CR47","doi-asserted-by":"crossref","unstructured":"Shen, Z., Xu, Y., Ni, B., Wang, M., Hu, J., Yang, X.: Crowd counting via adversarial cross-scale consistency pursuit. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5245\u20135254 (2018)","DOI":"10.1109\/CVPR.2018.00550"},{"key":"1531_CR48","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai,X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold,G., Gelly, S., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01531-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-024-01531-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-024-01531-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T16:06:26Z","timestamp":1724774786000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-024-01531-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":48,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["1531"],"URL":"https:\/\/doi.org\/10.1007\/s11554-024-01531-8","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"14 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2024","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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"153"}}