{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T09:35:28Z","timestamp":1781170528246,"version":"3.54.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319591469","type":"print"},{"value":"9783319591476","type":"electronic"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-59147-6_23","type":"book-chapter","created":{"date-parts":[[2017,5,16]],"date-time":"2017-05-16T21:04:08Z","timestamp":1494968648000},"page":"257-270","source":"Crossref","is-referenced-by-count":63,"title":["Automatic Learning of Gait Signatures for People Identification"],"prefix":"10.1007","author":[{"given":"Francisco Manuel","family":"Castro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manuel J.","family":"Mar\u00edn-Jim\u00e9nez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicol\u00e1s","family":"Guil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicol\u00e1s","family":"P\u00e9rez de la Blanca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2017,5,18]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Alotaibi, M., Mahmood, A.: Improved gait recognition based on specialized deep convolutional neural networks. In: 2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1\u20137, October 2015","DOI":"10.1109\/AIPR.2015.7444550"},{"issue":"10","key":"23_CR2","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.patrec.2009.03.014","volume":"30","author":"O Barnich","year":"2009","unstructured":"Barnich, O., Droogenbroeck, M.V.: Frontal-view gait recognition by intra- and inter-frame rectangle size distribution. Pattern Recogn. Lett. 30(10), 893\u2013901 (2009)","journal-title":"Pattern Recogn. Lett."},{"key":"23_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1007\/978-3-319-23192-1_61","volume-title":"Computer Analysis of Images and Patterns","author":"FM Castro","year":"2015","unstructured":"Castro, F.M., Mar\u00edn-Jim\u00e9nez, M.J., Guil, N.: Empirical study of audio-visual features fusion for gait recognition. In: Azzopardi, G., Petkov, N. (eds.) CAIP 2015. LNCS, vol. 9256, pp. 727\u2013739. Springer, Cham (2015). doi: 10.1007\/978-3-319-23192-1_61"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Castro, F.M., Mar\u00edn-Jim\u00e9nez, M., Guil Mata, N., Mu\u00f1oz Salinas, R.: Fisher motion descriptor for multiview gait recognition. Int. J. Patt. Recogn. Artif. Intell. 31(1) (2017)","DOI":"10.1142\/S021800141756002X"},{"key":"23_CR5","unstructured":"Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran, J., Catanzaro, B., Shelhamer, E.: cudnn: Efficient primitives for deep learning. CoRR abs\/1410.0759 (2014)"},{"key":"23_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/3-540-45103-X_50","volume-title":"Image Analysis","author":"G Farneb\u00e4ck","year":"2003","unstructured":"Farneb\u00e4ck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363\u2013370. Springer, Heidelberg (2003). doi: 10.1007\/3-540-45103-X_50"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"G\u00e1lai, B., Benedek, C.: Feature selection for lidar-based gait recognition. In: 2015 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), pp. 1\u20135 (2015)","DOI":"10.1109\/IWCIM.2015.7347076"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Guan, Y., Li, C.T.: A robust speed-invariant gait recognition system for walker and runner identification. In: International Conference on Biometrics (ICB), pp. 1\u20138 (2013)","DOI":"10.1109\/ICB.2013.6612965"},{"issue":"2","key":"23_CR9","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1109\/TPAMI.2006.38","volume":"28","author":"J Han","year":"2006","unstructured":"Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE PAMI 28(2), 316\u2013322 (2006)","journal-title":"IEEE PAMI"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778, June 2016","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"23_CR11","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.jvcir.2013.02.006","volume":"25","author":"M Hofmann","year":"2014","unstructured":"Hofmann, M., Geiger, J., Bachmann, S., Schuller, B., Rigoll, G.: The TUM gait from audio, image and depth (GAID) database: multimodal recognition of subjects and traits. J. Vis. Commun. Image Represent. 25(1), 195\u2013206 (2014)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Hossain, E., Chetty, G.: Multimodal feature learning for gait biometric based human identity recognition. In: Neural Information Processing, pp. 721\u2013728 (2013)","DOI":"10.1007\/978-3-642-42042-9_89"},{"issue":"3","key":"23_CR13","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1109\/TSMCC.2004.829274","volume":"34","author":"W Hu","year":"2004","unstructured":"Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 34(3), 334\u2013352 (2004)","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"issue":"1","key":"23_CR14","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2013","unstructured":"Ji, S., Xu, W., Yang, M., Yu, K.: 3D Convolutional Neural Networks for human action recognition. IEEE PAMI 35(1), 221\u2013231 (2013)","journal-title":"IEEE PAMI"},{"key":"23_CR15","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/978-1-4615-0913-4_11","volume-title":"Video-Based Surveillance Systems","author":"P KaewTraKulPong","year":"2002","unstructured":"KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Remagnino, P., Jones, G.A., Paragios, N., Regazzoni, C.S. (eds.) Video-Based Surveillance Systems, pp. 135\u2013144. Springer, New York (2002)"},{"key":"23_CR16","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097\u20131105 (2012)"},{"key":"23_CR17","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: NIPS, pp. 568\u2013576 (2014)"},{"key":"23_CR18","unstructured":"Soomro, K., Zamir, A.R., Shah, M.: UCF101: a dataset of 101 human action classes from videos in the wild. In: CRCV-TR-12-01, November 2012"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L.D., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: ICCV. IEEE (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., Lenc, K.: MatConvNet - convolutional neural networks for MATLAB. In: Proceedings of the ACM International Conference on Multimedia (2015)","DOI":"10.1145\/2733373.2807412"},{"issue":"3","key":"23_CR21","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1007\/s10851-014-0501-8","volume":"50","author":"T Whytock","year":"2014","unstructured":"Whytock, T., Belyaev, A., Robertson, N.: Dynamic distance-based shape features for gait recognition. J. Math. Imaging Vis. 50(3), 314\u2013326 (2014)","journal-title":"J. Math. Imaging Vis."},{"key":"23_CR22","unstructured":"Wu, Z., Huang, Y., Wang, L., Wang, X., Tan, T.: A comprehensive study on cross-view gait based human identification with deep CNNs. IEEE PAMI PP(99) (2016)"},{"issue":"11","key":"23_CR23","doi-asserted-by":"crossref","first-page":"1960","DOI":"10.1109\/TMM.2015.2477681","volume":"17","author":"Z Wu","year":"2015","unstructured":"Wu, Z., Huang, Y., Wang, L.: Learning representative deep features for image set analysis. IEEE Trans. Multimedia 17(11), 1960\u20131968 (2015)","journal-title":"IEEE Trans. Multimedia"},{"issue":"11","key":"23_CR24","doi-asserted-by":"crossref","first-page":"3568","DOI":"10.1016\/j.patcog.2014.04.014","volume":"47","author":"W Zeng","year":"2014","unstructured":"Zeng, W., Wang, C., Yang, F.: Silhouette-based gait recognition via deterministic learning. Pattern Recogn. 47(11), 3568\u20133584 (2014)","journal-title":"Pattern Recogn."}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-59147-6_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,24]],"date-time":"2019-09-24T14:55:06Z","timestamp":1569336906000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-59147-6_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319591469","9783319591476"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-59147-6_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}