{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:24:36Z","timestamp":1758266676374,"version":"3.37.3"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2016,11,12]],"date-time":"2016-11-12T00:00:00Z","timestamp":1478908800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61402429","61472390","11271361","11201472","11331012","71331005","71110107026"],"award-info":[{"award-number":["61402429","61472390","11271361","11201472","11331012","71331005","71110107026"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2018,6]]},"DOI":"10.1007\/s00521-016-2639-3","type":"journal-article","created":{"date-parts":[[2016,11,12]],"date-time":"2016-11-12T03:48:07Z","timestamp":1478922487000},"page":"1485-1494","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Pedestrian detection based on the privileged information"],"prefix":"10.1007","volume":"29","author":[{"given":"Fan","family":"Meng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiquan","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjie","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingfeng","family":"Niu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,11,12]]},"reference":[{"key":"2639_CR1","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.neucom.2013.09.045","volume":"129","author":"Z Qi","year":"2014","unstructured":"Qi Z, Tian Y, Shi Y (2014) A new classification model using privileged information and its application. Neurocomputing 129:146\u2013152","journal-title":"Neurocomputing"},{"key":"2639_CR2","doi-asserted-by":"crossref","unstructured":"Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: CVPR, pp. 511\u2013518","DOI":"10.1109\/CVPR.2001.990517"},{"key":"2639_CR3","doi-asserted-by":"crossref","unstructured":"Laptev I (2006) Improvements of object detection using boosted histograms. In: BMVC, pp. 949\u2013958","DOI":"10.5244\/C.20.97"},{"key":"2639_CR4","doi-asserted-by":"crossref","unstructured":"Pang J, Huang Q, Jiang S (2008) Multiple instance boost using graph embedding based decision stump for pedestrian detection. In: ECCV, pp. 541\u2013552","DOI":"10.1007\/978-3-540-88693-8_40"},{"key":"2639_CR5","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: CVPR, pp. 886\u2013893","DOI":"10.1109\/CVPR.2005.177"},{"key":"2639_CR6","doi-asserted-by":"crossref","unstructured":"Wang X, Han TX, Yan S (2009) An hog-lbp human detector with partial occlusion handling. In: ICCV, pp. 32\u201339","DOI":"10.1109\/ICCV.2009.5459207"},{"key":"2639_CR7","doi-asserted-by":"crossref","unstructured":"Felzenszwalb P, McAllester D, Ramanan D (2008) A discriminatively trained, multiscale, deformable part model. In: CVPR","DOI":"10.1109\/CVPR.2008.4587597"},{"key":"2639_CR8","doi-asserted-by":"crossref","unstructured":"Maji S, Berg A, Malik J (2008) Classification using intersection kernel support vector machines is efficient. In: CVPR, pp. 1\u20138","DOI":"10.1109\/CVPR.2008.4587630"},{"key":"2639_CR9","doi-asserted-by":"crossref","unstructured":"Sudowe P, Leibe B (2011) Efficient use of geometric constraints for sliding-window object detection in video. In: Proceedings of the 8th international conference on Computer vision systems, pp. 11\u201320","DOI":"10.1007\/978-3-642-23968-7_2"},{"key":"2639_CR10","doi-asserted-by":"crossref","unstructured":"Schwartz WR, Kembhavi A, Harwood D, Davis LS (2009) Human detection using partial least squares analysis. In: CVPR, pp. 24\u201331","DOI":"10.1109\/ICCV.2009.5459205"},{"key":"2639_CR11","unstructured":"Ding Y, Xiao J (2012) Contextual boost for pedestrian detection. In: CVPR, pp. 2895\u20132902"},{"key":"2639_CR12","doi-asserted-by":"crossref","unstructured":"Lampert CH, Blaschko MB, Hofmann T (2008) Beyond sliding windows: object localization by efficient subwindow search. In: CVPR","DOI":"10.1109\/CVPR.2008.4587586"},{"key":"2639_CR13","doi-asserted-by":"crossref","DOI":"10.1007\/0-387-34239-7","volume-title":"Estimation of dependences based on empirical data (information science and statistics)","author":"V Vapnik","year":"2006","unstructured":"Vapnik V (2006) Estimation of dependences based on empirical data (information science and statistics). Springer, New York"},{"issue":"5\u20136","key":"2639_CR14","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1016\/j.neunet.2009.06.042","volume":"22","author":"V Vapnik","year":"2009","unstructured":"Vapnik V, Vashist A (2009) A new learning paradigm: learning using privileged information. Neural Netw 22(5\u20136):544\u2013557","journal-title":"Neural Netw"},{"key":"2639_CR15","unstructured":"Pechyony D, Vapnik V (2010) On the theory of learning with privileged information. In: Advances in neural information processing systems 23"},{"key":"2639_CR16","unstructured":"Yang H, Patras I. Privileged information-based conditional regression forest for facial feature detection"},{"key":"2639_CR17","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/TPAMI.2007.1068","volume":"29","author":"S Jayadeva","year":"2007","unstructured":"Jayadeva S, Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29:905\u2013910","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2639_CR18","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/TPAMI.2011.155","volume":"34","author":"P Doll\u00e1r","year":"2012","unstructured":"Doll\u00e1r P, Wojek C, Schiele B, Perona P (2012) Pedestrian detection: an evaluation of the state of the art. IEEE Trans Pattern Anal Mach Intell 34:743\u2013761","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2639_CR19","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TNNLS.2011.2178324","volume":"23","author":"P Wang","year":"2012","unstructured":"Wang P, Shen C, Barnes N, Zheng H (2012) Fast and robust object detection using asymmetric totally corrective boosting. IEEE Trans Neural Netw Learn Syst 23:33\u201346","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2639_CR20","doi-asserted-by":"crossref","unstructured":"Yan J, Zhang X, Lei Z, Liao S, Li SZ (2013) Robust multi-resolution pedestrian detection in traffic scenes. In: CVPR, pp. 3033\u20133040","DOI":"10.1109\/CVPR.2013.390"},{"key":"2639_CR21","doi-asserted-by":"crossref","unstructured":"Benenson R, Mathias M, Timofte R, Gool LJV (2012) Pedestrian detection at 100 frames per second. In: CVPR, pp. 2903\u20132910","DOI":"10.1109\/CVPR.2012.6248017"},{"key":"2639_CR22","doi-asserted-by":"crossref","unstructured":"Enzweiler M, Eigenstetter A, Schiele B, Gavrila DM (2010) Multi-cue pedestrian classification with partial occlusion handling. In: CVPR, pp. 990\u2013997","DOI":"10.1109\/CVPR.2010.5540111"},{"key":"2639_CR23","unstructured":"Torralba A, Murphy K, Freeman W. Contextual models for object detection using boosted random fields. In: Advances in neural information processing systems"},{"key":"2639_CR24","unstructured":"Sermanet P, Kavukcuoglu K, Chintala S, LeCun Y. Pedestrian detection with unsupervised multi-stage feature learning. arXiv preprint arXiv:1212.0142"},{"key":"2639_CR25","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1186\/1471-2105-12-311","volume":"12","author":"R Chen","year":"2011","unstructured":"Chen R, Chen W, Yang S, Wu D, Wang Y, Tian Y, Shi Y (2011) Rigorous assessment and integration of the sequence and structure based features to predict hot spots. BMC Bioinformatics 12:311\u2013324","journal-title":"BMC Bioinformatics"},{"key":"2639_CR26","volume-title":"Combining labeled and unlabeled data with co-training","author":"A Blum","year":"1998","unstructured":"Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. Morgan Kaufmann Publishers, Los Altos"},{"key":"2639_CR27","doi-asserted-by":"crossref","unstructured":"Walk S, Majer N, Schindler K, Schiele B (2010) New features and insights for pedestrian detection. In: CVPR, pp. 1030\u20131037","DOI":"10.1109\/CVPR.2010.5540102"},{"key":"2639_CR28","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B, Schmid C (2006) Human detection using oriented histograms of flow and appearance. In: ECCV, pp. 428\u2013441","DOI":"10.1007\/11744047_33"},{"key":"2639_CR29","doi-asserted-by":"crossref","unstructured":"Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2","DOI":"10.1145\/1961189.1961199"},{"key":"2639_CR30","unstructured":"Pechyony D, Izmailov R, Vashist A, Vapnik V (2010) Smo-style algorithms for learning using privileged information. In: DMIN, pp. 235\u2013241"},{"key":"2639_CR31","doi-asserted-by":"crossref","unstructured":"Sabzmeydani P, Mori G (2007) Detecting pedestrians by learning shapelet features. In: CVPR, pp. 1\u20138","DOI":"10.1109\/CVPR.2007.383134"},{"key":"2639_CR32","doi-asserted-by":"crossref","unstructured":"Lin Z, Davis LS (2008) A pose-invariant descriptor for human detection and segmentation. In: ECCV, pp. 423\u2013436","DOI":"10.1007\/978-3-540-88693-8_31"},{"key":"2639_CR33","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r P, Tu Z, Tao H, Belongie S (2007) Feature mining for image classification. In: CVPR","DOI":"10.1109\/CVPR.2007.383046"},{"key":"2639_CR34","doi-asserted-by":"crossref","unstructured":"Wojek C, Schiele B (2008) A performance evaluation of single and multi-feature people detection. In: Proceedings of the 30th DAGM symposium on pattern recognition, pp. 82\u201391","DOI":"10.1007\/978-3-540-69321-5_9"},{"key":"2639_CR35","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2010","unstructured":"Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32:1627\u20131645","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2639_CR36","doi-asserted-by":"crossref","unstructured":"Bar-Hillel A, Levi D, Krupka E, Goldberg C (2010) Part-based feature synthesis for human detection. In: ECCV (4), pp. 127-142","DOI":"10.1007\/978-3-642-15561-1_10"},{"key":"2639_CR37","doi-asserted-by":"crossref","unstructured":"Dollar P, Belongie S, Perona P (2010) The fastest pedestrian detector in the west. In: Proceedings of the British machine vision conference, pp. 68.1\u201368.11","DOI":"10.5244\/C.24.68"},{"key":"2639_CR38","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r P, Tu Z, Perona P, Belongie S (2009) Integral channel features. In: British machine vision conference","DOI":"10.5244\/C.23.91"},{"key":"2639_CR39","unstructured":"Park D, Zitnick CL, Ramanan D, Doll\u00e1r P. Exploring weak stabilization for motion feature extraction"},{"key":"2639_CR40","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r P, Appel R, Kienzle W (2012) Crosstalk cascades for frame-rate pedestrian detection. In: ECCV. Springer, pp. 645\u2013659","DOI":"10.1007\/978-3-642-33709-3_46"},{"key":"2639_CR41","unstructured":"Ouyang W, Wang X (2012) A discriminative deep model for pedestrian detection with occlusion handling. In: CVPR, pp. 3258\u20133265"},{"key":"2639_CR42","doi-asserted-by":"crossref","unstructured":"Shen C, Wang P, Paisitkriangkrai S, van den Hengel A (2013) Training effective node classifiers for cascade classification. Int J Comput Vis, 1\u201322","DOI":"10.1007\/s11263-013-0608-1"},{"key":"2639_CR43","doi-asserted-by":"crossref","unstructured":"Benenson R, Mathias M, Tuytelaars T, Van Gool L (2013) Seeking the strongest rigid detector. In: Proceedings of IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2013.470"},{"key":"2639_CR44","doi-asserted-by":"crossref","unstructured":"Chen G, Ding Y, Xiao J, Han TX (2013) Detection evolution with multi-order contextual co-occurrence. In: CVPR, pp. 1798\u20131805","DOI":"10.1109\/CVPR.2013.235"},{"key":"2639_CR45","doi-asserted-by":"crossref","unstructured":"Yan J, Zhang X, Lei Z, Liao S, Li SZ (2013) Robust multi-resolution pedestrian detection in traffic scenes. In: CVPR, pp. 3033\u20133040","DOI":"10.1109\/CVPR.2013.390"},{"key":"2639_CR46","unstructured":"Nam W, Han B, Han JH (2011) Improving object localization using macrofeature layout selection. In: ICCV, pp. 1801\u20131808"},{"key":"2639_CR47","unstructured":"Paisitkriangkrai S, Shen C, Hengel AVD. Efficient pedestrian detection by directly optimize the partial area under the roc curve. arXiv preprint arXiv:1310.0900"},{"key":"2639_CR48","unstructured":"Ouyang W, Zeng X, Wang X. Modeling mutual visibility relationship in pedestrian detection"},{"key":"2639_CR49","doi-asserted-by":"crossref","unstructured":"Ess A, Leibe B, Schindler K, van Gool L (2008) A mobile vision system for robust multi-person tracking. In: CVPR","DOI":"10.1109\/CVPR.2008.4587581"},{"key":"2639_CR50","doi-asserted-by":"crossref","unstructured":"Wojek C, Walk S, Schiele B (2009) Multi-cue onboard pedestrian detection. In: CVPR, pp. 1\u20138. http:\/\/www.d2.mpi-inf.mpg.de\/tud-brussels","DOI":"10.1109\/CVPR.2009.5206638"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-016-2639-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-016-2639-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-016-2639-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,27]],"date-time":"2020-09-27T08:04:50Z","timestamp":1601193890000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-016-2639-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,12]]},"references-count":50,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2018,6]]}},"alternative-id":["2639"],"URL":"https:\/\/doi.org\/10.1007\/s00521-016-2639-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2016,11,12]]}}}