{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T00:03:43Z","timestamp":1649203423172},"reference-count":25,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2018,5,1]]},"DOI":"10.1587\/transinf.2017mvp0001","type":"journal-article","created":{"date-parts":[[2018,4,30]],"date-time":"2018-04-30T18:25:35Z","timestamp":1525112735000},"page":"1222-1231","source":"Crossref","is-referenced-by-count":0,"title":["Training of CNN with Heterogeneous Learning for Multiple Pedestrian Attributes Recognition Using Rarity Rate"],"prefix":"10.1587","volume":"E101.D","author":[{"given":"Hiroshi","family":"FUKUI","sequence":"first","affiliation":[{"name":"Chubu University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takayoshi","family":"YAMASHITA","sequence":"additional","affiliation":[{"name":"Chubu University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuji","family":"YAMAUCHI","sequence":"additional","affiliation":[{"name":"Chubu University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hironobu","family":"FUJIYOSHI","sequence":"additional","affiliation":[{"name":"Chubu University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroshi","family":"MURASE","sequence":"additional","affiliation":[{"name":"Nagoya University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] D. Ger\u00f3nimo, A.M. L\u00f3pez, A.D. Sappa, and T. Graf, \u201cSurvey of Pedestrian Detection for Advanced Driver Assistance Systems,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.32, no.7, pp.1239-1258, 2010. 10.1109\/tpami.2009.122","DOI":"10.1109\/TPAMI.2009.122"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] N. Dalal, and B. Triggs, \u201cHistograms of oriented gradients for human detection,\u201d Computer Vision and Pattern Recognition, pp.886-893, 2005. 10.1109\/cvpr.2005.177","DOI":"10.1109\/CVPR.2005.177"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] P. Felzenszwalb, D. McAllester, and D. Ramaman, \u201cA Discriminatively Trained, Multi scale, Deformable Part Model,\u201d Computer Vision and Pattern Recognition, pp.1-8, 2008. 10.1109\/cvpr.2008.4587597","DOI":"10.1109\/CVPR.2008.4587597"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] X. Wang, T.X. Han, and S. Yan, \u201cAn HOG-LBP Human Detection with Partial Occlusion,\u201d International Conference on Computer Vision, pp.32-39, 2009. 10.1109\/iccv.2009.5459207","DOI":"10.1109\/ICCV.2009.5459207"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] W. Nam, B. Han, and J.H. Han, \u201cImproving Object Localization Using Macrofeature Layout Selection,\u201d International Conference on Computer Vision Workshop on Visual Surveillance, pp.1801-1808, 2011. 10.1109\/iccvw.2011.6130467","DOI":"10.1109\/ICCVW.2011.6130467"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] J. Marin, D. Vazquez, A.M. Lopez, J. Amores, and B. Leibe, \u201cRandom Forests of Local Experts for Pedestrian Detection,\u201d International Conference on Computer Vision, pp.2592-2599, 2013. 10.1109\/iccv.2013.322","DOI":"10.1109\/ICCV.2013.322"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] H. Fukui, T. Yamashita, Y. Yamauchi, H. Fujiyoshi, and H. Murase, \u201cPedestrian Detection Based on Deep Convolutional Neural Network with Ensemble Inference Networks,\u201d 2015 IEEE Intelligent Vehicles Symposium (IV), pp.223-228, 2015. 10.1109\/ivs.2015.7225690","DOI":"10.1109\/IVS.2015.7225690"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] Y. Tian, P. Luo, X. Wang, and X. Tang, \u201cPedestrian Detection aided by Deep Learning Semantic Tasks,\u201d Computer Vision and Pattern Recognition, pp.5079-5087, 2015. 10.1109\/cvpr.2015.7299143","DOI":"10.1109\/CVPR.2015.7299143"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] J. Hosang, M. Omran, R. Benenson, and B. Schiele, \u201cTaking a Deeper Look at Pedestrian,\u201d Computer Vision and Pattern Recognition, pp.4073-4082, 2015. 10.1109\/cvpr.2015.7299034","DOI":"10.1109\/CVPR.2015.7299034"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] H. Brodsky, and A.S. Hakkert, \u201cRisk of a road accident in rainy weather,\u201d Accident Analysis & Prevention, vol.20, no.3, pp.161-176, 1988. 10.1016\/0001-4575(88)90001-2","DOI":"10.1016\/0001-4575(88)90001-2"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] A. Argyriou, T. Evgeniou, and M. Pontil, \u201cConvex Multi-task Feature Learning,\u201d Mach. Learn., vol.73, no.3, pp.243-272, 2008. 10.1007\/s10994-007-5040-8","DOI":"10.1007\/s10994-007-5040-8"},{"key":"12","unstructured":"[12] X. Yang, S. Kim, and F.P. Xing, \u201cHeterogeneous Multi-task Learning with Sparsity Constrain,\u201d Advances in Neural Information Processing Systems 22, pp.2151-2159, 2009."},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] S. Li, Z.-Q. Liu, and A.B. Chan, \u201cHeterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network,\u201d Computer Vision and Pattern Recognition, pp.488-495, 2014. 10.1109\/cvprw.2014.78","DOI":"10.1109\/CVPRW.2014.78"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] Z. Zhang, P. Luo, C.C. Loy, and X. Tang, \u201cFacial Landmark Detection by Deep Multi-task Learning,\u201d Computer Vision-ECCV 2014, Lecture Notes in Computer Science, vol.8694, pp.94-108, Springer International Publishing, Cham, 2014. 10.1007\/978-3-319-10599-4_7","DOI":"10.1007\/978-3-319-10599-4_7"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] F.J. Pulgar, A.J. Rivera, F. Charte, and M.J.D. Jesus, \u201cOn the Impact of Imbalanced Data in Convolutional Neutral Networks Performance,\u201d Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science, vol.10334, pp.220-232, Springer International Publishing, Cham, 2017. 10.1007\/978-3-319-59650-1_19","DOI":"10.1007\/978-3-319-59650-1_19"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] C. Huang, Y. Li, C.C. Loy, and X. Tang, \u201cLearning Deep Representation for Imbalanced Classification,\u201d IEEE Computer Vision and Pattern Recognition, pp.5375-5384, 2016. 10.1109\/cvpr.2016.580","DOI":"10.1109\/CVPR.2016.580"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] J. Zhu, S. Liao, D. Yi, Z. Lei, and S.Z. Li, \u201cMulti-label CNN Based Pedestrian Attribute Learning for Soft Biometrics,\u201d International Conference on Biometrics, pp.535-540, 2015. 10.1109\/icb.2015.7139070","DOI":"10.1109\/ICB.2015.7139070"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] D. Li, X. Chen, and K. Huang, \u201cMulti-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios,\u201d International Association of Pattern Recognition on Asian Conference on Pattern Recognition, pp.111-115, 2015. 10.1109\/acpr.2015.7486476","DOI":"10.1109\/ACPR.2015.7486476"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] E. Bekele, C. Narber, and W. Lawson, \u201cMulti-attribute Residual Network (MAResNet) for Soft-biometrics Recognition in Surveillance Scenarios,\u201d IEEE International Conference on Automatic Face and Gesture Recognition, pp.386-393, 2017. 10.1109\/fg.2017.55","DOI":"10.1109\/FG.2017.55"},{"key":"20","unstructured":"[20] A. Krizhevsky, S. Ilva, and G.E. Hinton, \u201cImageNet Classification with Deep Convolutional Neural Network,\u201d Advances in Neural Information Processing System 25, pp.1097-1105, 2012."},{"key":"21","unstructured":"[21] K. Simonyan, and A. Zisserman, \u201cVery Deep Convolutional Networks for Large-Scale Image Recognition,\u201d International Conference on Learning Representations, 2015."},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] E. Ricci, J. Varadarajan, R. Subramanian, S. Rota-Bulo, N. Ahuja, and O. Lanz, \u201cUncovering Interactions and Interactors: Joint Estimation of Head, Body Orientation and F-formations from Surveillance Videos,\u201d Association for Computing Machine, pp.4660-4668,2015. 10.1109\/iccv.2015.529","DOI":"10.1109\/ICCV.2015.529"},{"key":"23","unstructured":"[23] A. Bearman, and C. Dong, \u201cHuman Pose Estimation and Activity Classification Using Convolutional Neural Networks,\u201d CS231n Course Project Reports, 2015."},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] P. Dollar, C. Wojek, B. Schiele, and P. Perona, \u201cPedestrian Detection: An Evaluation of the State of the Art,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.34, no.4, pp.743-761, 2012. 10.1109\/tpami.2011.155","DOI":"10.1109\/TPAMI.2011.155"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] D.E. Rumelhart, G.E. Hinton, and R.J. Williams, \u201cLearning representations by back-propagating errors,\u201d Nature., vol.323, no.6088, pp.533-536, 1986. 10.1038\/323533a0","DOI":"10.1038\/323533a0"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E101.D\/5\/E101.D_2017MVP0001\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,17]],"date-time":"2019-10-17T03:55:06Z","timestamp":1571284506000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E101.D\/5\/E101.D_2017MVP0001\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,1]]},"references-count":25,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2018]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2017mvp0001","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,1]]}}}