{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T01:31:54Z","timestamp":1775266314718,"version":"3.50.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030010539","type":"print"},{"value":"9783030010546","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,11,9]],"date-time":"2018-11-09T00:00:00Z","timestamp":1541721600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-01054-6_9","type":"book-chapter","created":{"date-parts":[[2018,11,8]],"date-time":"2018-11-08T09:47:05Z","timestamp":1541670425000},"page":"136-149","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Classification-Regression Deep Learning Model for People Counting"],"prefix":"10.1007","author":[{"given":"Bolei","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbin","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Garibaldi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoping","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,11,9]]},"reference":[{"key":"9_CR1","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":"9_CR2","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D., Li, W.: Deep reconstruction-classification networks for unsupervised domain adaptation. In: European Conference on Computer Vision, pp. 597\u2013613. Springer (2016)","DOI":"10.1007\/978-3-319-46493-0_36"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Zhang, C., Li, H., Wang, X., Yang, X.: Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298684"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhang, H., Yang, L., Liu, S., Cao, X.: Deep people counting in extremely dense crowds. In: Proceedings of the 23rd ACM international conference on Multimedia, pp. 1299\u20131302. ACM (2015)","DOI":"10.1145\/2733373.2806337"},{"key":"9_CR6","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"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Onoro-Rubio, D., L\u00f3pez-Sastre, R.J.: Towards perspective-free object counting with deep learning. In: European Conference on Computer Vision, pp. 615\u2013629. Springer (2016)","DOI":"10.1007\/978-3-319-46478-7_38"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Sindagi, V.A., Patel, V.M.: CNN-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. arXiv preprint arXiv:1707.09605 (2017)","DOI":"10.1109\/AVSS.2017.8078491"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Chen, K., K\u00e4m\u00e4r\u00e4inen, J.-K.: Learning with ambiguous label distribution for apparent age estimation. In: Asian Conference on Computer Vision, pp. 330\u2013343. Springer (2016)","DOI":"10.1007\/978-3-319-54187-7_22"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Geng, X., Wang, Q., Xia, Y.: Facial age estimation by adaptive label distribution learning. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 4465\u20134470. IEEE (2014)","DOI":"10.1109\/ICPR.2014.764"},{"issue":"6","key":"9_CR11","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.1109\/TIP.2017.2689998","volume":"26","author":"B-B Gao","year":"2017","unstructured":"Gao, B.-B., Xing, C., Xie, C.-W., Wu, J., Geng, X.: Deep label distribution learning with label ambiguity. IEEE Trans. Image Process. 26(6), 2825\u20132838 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Xu, B., Qiu, G.: Crowd density estimation based on rich features and random projection forest. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1\u20138. IEEE (2016)","DOI":"10.1109\/WACV.2016.7477682"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: Ninth IEEE International Conference on Computer Vision, Proceedings, pp. 734\u2013741. IEEE (2003)","DOI":"10.1109\/ICCV.2003.1238422"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol.\u00a01, pp. 886\u2013893. IEEE (2005)","DOI":"10.1109\/CVPR.2005.177"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Zhao, T., Nevatia, R.: Bayesian human segmentation in crowded situations. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings, vol.\u00a02, pp. II\u2013459. IEEE (2003)","DOI":"10.1109\/CVPR.2003.1211503"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Wu, B., Nevatia, R.: Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol.\u00a01, pp. 90\u201397. IEEE (2005)","DOI":"10.1109\/ICCV.2005.74"},{"issue":"6","key":"9_CR17","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.S 31(6), 645\u2013654 (2001)","journal-title":"IEEE Trans. Syst., Man Cybern., Part A: Syst. Hum.S"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Brostow, G.J., Cipolla, R.: Unsupervised bayesian detection of independent motion in crowds. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.\u00a01, pp. 594\u2013601. IEEE (2006)","DOI":"10.1109\/CVPR.2006.320"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Rabaud, V., Belongie, S.: Counting crowded moving objects. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol.\u00a01, pp. 705\u2013711. IEEE (2006)","DOI":"10.1109\/CVPR.2006.92"},{"issue":"10","key":"9_CR20","doi-asserted-by":"publisher","first-page":"3038","DOI":"10.1016\/j.patcog.2015.02.009","volume":"48","author":"Homa Foroughi","year":"2015","unstructured":"Foroughi, H., Ray, N., Zhang, H.: Robust people counting using sparse representation and random projection. Pattern Recogn. (2015)","journal-title":"Pattern Recognition"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Paragios, N., Ramesh, V.: A MRF-based approach for real-time subway monitoring. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol.\u00a01, pp. I\u20131034. IEEE (2001)","DOI":"10.1109\/CVPR.2001.990644"},{"issue":"4","key":"9_CR22","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1109\/3477.775269","volume":"29","author":"S-Y Cho","year":"1999","unstructured":"Cho, S.-Y., Chow, T.W., Leung, C.-T.: A neural-based crowd estimation by hybrid global learning algorithm. IEEE Trans. Syst., Man, Cybern., Part B: Cybern. 29(4), 535\u2013541 (1999)","journal-title":"IEEE Trans. Syst., Man, Cybern., Part B: Cybern."},{"issue":"1","key":"9_CR23","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1049\/ecej:19950106","volume":"7","author":"AC Davies","year":"1995","unstructured":"Davies, A.C., Yin, J.H., Velastin, S.A.: Crowd monitoring using image processing. Electron. Commun. Eng. J. 7(1), 37\u201347 (1995)","journal-title":"Electron. Commun. Eng. J."},{"issue":"1","key":"9_CR24","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/0165-1684(96)00075-8","volume":"53","author":"CS Regazzoni","year":"1996","unstructured":"Regazzoni, C.S., Tesei, A.: Distributed data fusion for real-time crowding estimation. Signal Process. 53(1), 47\u201363 (1996)","journal-title":"Signal Process."},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Marana, A., da Costa, L., Lotufo, R., Velastin, S.: On the efficacy of texture analysis for crowd monitoring. In: International Symposium on Computer Graphics, Image Processing, and Vision, Proceedings, SIBGRAPI 1998, pp. 354\u2013361. IEEE (1998)","DOI":"10.1109\/SIBGRA.1998.722773"},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"Chan, A.B., Liang, Z.-S., Vasconcelos, N.: Privacy preserving crowd monitoring: Counting people without people models or tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1\u20137. IEEE (2008)","DOI":"10.1109\/CVPR.2008.4587569"},{"key":"9_CR27","doi-asserted-by":"crossref","unstructured":"Marsden, M., McGuiness, K., Little, S., O\u2019Connor, N.E.: Fully convolutional crowd counting on highly congested scenes. arXiv preprint arXiv:1612.00220 (2016)","DOI":"10.5220\/0006097300270033"},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Boominathan, L., Kruthiventi, S.S., Babu, R.V.: Crowdnet: a deep convolutional network for dense crowd counting. In: Proceedings of the 2016 ACM on Multimedia Conference, pp. 640\u2013644. ACM (2016)","DOI":"10.1145\/2964284.2967300"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localised crowd counting. In: BMVC, vol.\u00a01, no.\u00a02, p.\u00a03 (2012)","DOI":"10.5244\/C.26.21"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Chen, K., Gong, S., Xiang, T., Loy, C.C.: Cumulative attribute space for age and crowd density estimation. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2467\u20132474. IEEE (2013)","DOI":"10.1109\/CVPR.2013.319"},{"key":"9_CR31","unstructured":"Kumagai, S., Hotta, K., Kurita, T.: Mixture of counting cnns: Adaptive integration of cnns specialized to specific appearance for crowd counting. arXiv preprint arXiv:1703.09393 (2017)"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Sam, D.B., Surya, S., Babu, R.V.: Switching convolutional neural network for crowd counting. arXiv preprint arXiv:1708.00199 (2017)","DOI":"10.1109\/CVPR.2017.429"},{"key":"9_CR33","unstructured":"Sheng, B., Shen, C., Lin, G., Li, J., Yang, W., Sun, C.: Crowd counting via weighted VLAD on dense attribute feature maps. IEEE Trans. Circuits Syst. Video Technol. (2016)"}],"container-title":["Advances in Intelligent Systems and Computing","Intelligent Systems and Applications"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01054-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:27:55Z","timestamp":1775262475000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-01054-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,9]]},"ISBN":["9783030010539","9783030010546"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01054-6_9","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"value":"2194-5357","type":"print"},{"value":"2194-5365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,9]]},"assertion":[{"value":"IntelliSys","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Proceedings of SAI Intelligent Systems Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"London","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"intellisys2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/saiconference.com\/IntelliSys2018\/CallforPapers","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}