{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T15:52:19Z","timestamp":1777564339570,"version":"3.51.4"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2017,7,17]],"date-time":"2017-07-17T00:00:00Z","timestamp":1500249600000},"content-version":"tdm","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":["61375038"],"award-info":[{"award-number":["61375038"]}],"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":[[2019,4]]},"DOI":"10.1007\/s00521-017-3152-z","type":"journal-article","created":{"date-parts":[[2017,7,16]],"date-time":"2017-07-16T23:44:11Z","timestamp":1500248651000},"page":"1189-1200","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Adaptive pedestrian detection by predicting classifier"],"prefix":"10.1007","volume":"31","author":[{"given":"Song","family":"Tang","sequence":"first","affiliation":[]},{"given":"Mao","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Pei","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xudong","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,7,17]]},"reference":[{"key":"3152_CR1","doi-asserted-by":"crossref","unstructured":"Andriluka M, Roth S, Schiele B (2009) Pictorial structures revisited: people detection and articulated pose estimation. In: Conference on computer vision and pattern recognition (CVPR), pp 1014\u20131021","DOI":"10.1109\/CVPR.2009.5206754"},{"key":"3152_CR2","doi-asserted-by":"crossref","unstructured":"Caseiro R, Henriques JF, Martins P, Batista J (2015) Beyond the shortest path: unsupervised domain adaptation by sampling subspaces along the spline flow. In: Conference on computer vision and pattern recognition (CVPR), pp 3846\u20133854","DOI":"10.1109\/CVPR.2015.7299009"},{"key":"3152_CR3","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Conference on computer vision and pattern recognition (CVPR), pp 886\u2013893","DOI":"10.1109\/CVPR.2005.177"},{"issue":"8","key":"3152_CR4","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TPAMI.2014.2300479","volume":"36","author":"P Doll\u00e1r","year":"2014","unstructured":"Doll\u00e1r P, Appel R, Belongie S, Perona P (2014) Fast feature pyramids for object detection. TIEEE Tran Pattern Anal Mach Intell (PAMI) 36(8):1532\u20131545","journal-title":"TIEEE Tran Pattern Anal Mach Intell (PAMI)"},{"issue":"2","key":"3152_CR5","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis (IJCV) 88(2):303\u2013338","journal-title":"Int J Comput Vis (IJCV)"},{"issue":"9","key":"3152_CR6","doi-asserted-by":"publisher","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. TIEEE Trans Pattern Anal Mach Intell (PAMI) 32(9):1627\u20131645","journal-title":"TIEEE Trans Pattern Anal Mach Intell (PAMI)"},{"key":"3152_CR7","doi-asserted-by":"crossref","unstructured":"Gall J, Lempitsky V (2013) Class-specific hough forests for object detection. In: Conference on computer vision and pattern recognition (CVPR), pp 143\u2013157","DOI":"10.1007\/978-1-4471-4929-3_11"},{"key":"3152_CR8","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik, J (2014a) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Conference on computer vision and pattern recognition (CVPR), pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"3152_CR9","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014b) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Conference on computer vision and pattern recognition (CVPR), pp 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"3152_CR10","unstructured":"Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. In: Conference on computer vision and pattern recognition (CVPR), pp 2066\u20132073"},{"key":"3152_CR11","doi-asserted-by":"crossref","unstructured":"Gopalan R, Li R, Chellappa R (2011) Domain adaptation for object recognition: an unsupervised approach. In: International conference on computer vision (ICCV), pp 999\u20131006","DOI":"10.1109\/ICCV.2011.6126344"},{"key":"3152_CR12","doi-asserted-by":"crossref","unstructured":"Gould S, Fulton R, Koller, D (2009) Decomposing a scene into geometric and semantically consistent regions. In: International Conference on computer vision (ICCV), pp 1\u20138","DOI":"10.1109\/ICCV.2009.5459211"},{"key":"3152_CR13","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: International conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2015.123"},{"key":"3152_CR14","doi-asserted-by":"crossref","unstructured":"Jiang W, Zavesky E, Chang SF, Loui A (2008) Cross-domain learning methods for high-level visual concept classification. In: International conference on image processing (ICIP), pp 161\u2013164","DOI":"10.1109\/ICIP.2008.4711716"},{"key":"3152_CR15","unstructured":"Kate S, Brian K, Mario F, Trevor D (2010) Adapting visual category models to new domains. In: European conference on computer vision (ECCV), pp 213\u2013226"},{"key":"3152_CR16","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems (NIPS), pp 1097\u20131105"},{"issue":"4","key":"3152_CR17","first-page":"1020","volume":"13","author":"X Li","year":"2015","unstructured":"Li X, Ye M, Fu M, Xu P, Li T (2015) Domain adaption of vehicle detector based on convolutional neural networks. IJCAS 13(4):1020\u20131031","journal-title":"IJCAS"},{"key":"3152_CR18","doi-asserted-by":"crossref","unstructured":"Malisiewicz T, Gupta A, Efros A et\u00a0al. (2011) Ensemble of exemplar-svms for object detection and beyond. In: International conference on computer vision (ICCV), pp 89\u201396","DOI":"10.1109\/ICCV.2011.6126229"},{"key":"3152_CR19","doi-asserted-by":"crossref","unstructured":"Nair V, Clark JJ (2004) An unsupervised, online learning framework for moving object detection. In: Conference on computer vision and pattern recognition (CVPR), pp 317\u2013324","DOI":"10.1109\/CVPR.2004.1315181"},{"key":"3152_CR20","doi-asserted-by":"crossref","unstructured":"Oren M, Papageorgiou C, Sinha P, Osuna E, Poggio T (1997) Pedestrian detection using wavelet templates. In: Conference on computer vision and pattern recognition (CVPR), pp 193\u201399","DOI":"10.1109\/CVPR.1997.609319"},{"key":"3152_CR21","doi-asserted-by":"crossref","unstructured":"Overett G, Petersson L, Brewer N, Andersso, L, Pettersson N (2008) A new pedestrian dataset for supervised learning. In: IVS, pp 373\u2013378","DOI":"10.1109\/IVS.2008.4621297"},{"issue":"5","key":"3152_CR22","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.1109\/TIP.2010.2103951","volume":"20","author":"J Pang","year":"2011","unstructured":"Pang J, Huang Q, Yan S, Jiang S, Qin L (2011) Transferring boosted detectors towards viewpoint and scene adaptiveness. IEEE Trans Image Process (TIP) 20(5):1388\u20131400","journal-title":"IEEE Trans Image Process (TIP)"},{"key":"3152_CR23","doi-asserted-by":"crossref","unstructured":"Schulter S, Leistner C, Wohlhart P, Roth PM, Bischof H (2014) Accurate object detection with joint classification-regression random forests. In: Conference on computer vision and pattern recognition (CVPR), pp 923\u2013930","DOI":"10.1109\/CVPR.2014.123"},{"key":"3152_CR24","doi-asserted-by":"crossref","unstructured":"Sermanet P, Kavukcuoglu K, Chintala S, LeCun Y (2013) Pedestrian detection with unsupervised multi-stage feature learning. In: Conference on computer vision and pattern recognition (CVPR), pp 3626\u20133633","DOI":"10.1109\/CVPR.2013.465"},{"key":"3152_CR25","doi-asserted-by":"crossref","unstructured":"Sun Y, Wang X, Tang X (2013) Deep convolutional network cascade for facial point detection. In: Conference on computer vision and pattern recognition (CVPR), pp 3476\u20133483","DOI":"10.1109\/CVPR.2013.446"},{"key":"3152_CR26","doi-asserted-by":"crossref","unstructured":"Tian Y, Luo P, Wang X, Tang X (2015) Pedestrian detection aided by deep learning semantic tasks. In: Conference on computer vision and pattern recognition (CVPR), pp 5079\u20135087","DOI":"10.1109\/CVPR.2015.7299143"},{"key":"3152_CR27","doi-asserted-by":"crossref","unstructured":"Wang M, Li W, Wang X (2012) Transferring a generic pedestrian detector towards specific scenes. In: Conference on computer vision and pattern recognition (CVPR), pp 3274\u20133281","DOI":"10.1109\/CVPR.2012.6248064"},{"key":"3152_CR28","doi-asserted-by":"crossref","unstructured":"Wang M, Wang X (2011) Automatic adaptation of a generic pedestrian detector to a specific traffic scene. In: Conference on computer vision and pattern recognition (CVPR), pp 3401\u20133408","DOI":"10.1109\/CVPR.2011.5995698"},{"issue":"2","key":"3152_CR29","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1109\/TPAMI.2013.124","volume":"36","author":"X Wang","year":"2014","unstructured":"Wang X, Wang M, Li W (2014) Scene-specific pedestrian detection for static video surveillance. TIEEE Trans Pattern Anal Mach Intell (PAMI) 36(2):361\u2013374","journal-title":"TIEEE Trans Pattern Anal Mach Intell (PAMI)"},{"key":"3152_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2824-4","author":"Y Wu","year":"2016","unstructured":"Wu Y, Wang L, Cui F, Zhai H, Dong B, Wang JY (2016) Cross-model convolutional neural network for multiple modality data representation. Neural Comput Appl. doi:\n                    10.1007\/s00521-016-2824-4","journal-title":"Neural Comput Appl"},{"key":"3152_CR31","doi-asserted-by":"crossref","unstructured":"Yang J, Yan R, Hauptmann AG (2007) Cross-domain video concept detection using adaptive svms. In: ACM international conference on multimedia (ACMM), pp 188\u2013197","DOI":"10.1145\/1291233.1291276"},{"key":"3152_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2783-9","author":"Z Yin","year":"2016","unstructured":"Yin Z, Kong D, Shao G, Ning X, Jin W, Wang JY (2016) A-optimal convolutional neural network. Neural Comput Appl. doi:\n                    10.1007\/s00521-016-2783-9","journal-title":"Neural Comput Appl"},{"key":"3152_CR33","doi-asserted-by":"crossref","unstructured":"Zeng X, Ouyang W, Wang M, Wang X (2014) Deep learning of scene-specific classifier for pedestrian detection. In: European conference on computer vision (ECCV), pp 472\u2013487","DOI":"10.1007\/978-3-319-10578-9_31"},{"key":"3152_CR34","doi-asserted-by":"crossref","unstructured":"Zeng X, Ouyang W, Wang X (2013) Multi-stage contextual deep learning for pedestrian detection. In: International conference on computer vision (ICCV), pp 121\u2013128","DOI":"10.1109\/ICCV.2013.22"},{"issue":"2","key":"3152_CR35","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1109\/TII.2016.2605629","volume":"13","author":"H Zhang","year":"2017","unstructured":"Zhang H, Cao X, Ho JK, Chow TW (2017) Object-level video advertising: an optimization framework. IEEE Trans Ind Inform 13(2):520\u2013531","journal-title":"IEEE Trans Ind Inform"},{"issue":"2","key":"3152_CR36","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1109\/TII.2016.2601521","volume":"13","author":"H Zhang","year":"2017","unstructured":"Zhang H, Li J, Ji Y, Yue H (2017) Understanding subtitles by character-level sequence-to-sequence learning. IEEE Trans Ind Inform 13(2):616\u2013624","journal-title":"IEEE Trans Ind Inform"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-3152-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-017-3152-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-3152-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,17]],"date-time":"2019-04-17T04:15:00Z","timestamp":1555474500000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-017-3152-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,17]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,4]]}},"alternative-id":["3152"],"URL":"https:\/\/doi.org\/10.1007\/s00521-017-3152-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,7,17]]},"assertion":[{"value":"18 April 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2017","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and\/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, Adaptive Pedestrian Detection by Predicting Classifier.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}