{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T11:21:36Z","timestamp":1725794496174},"reference-count":34,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,6]]},"DOI":"10.1109\/ivs.2017.7995953","type":"proceedings-article","created":{"date-parts":[[2017,7,31]],"date-time":"2017-07-31T16:40:12Z","timestamp":1501519212000},"page":"1700-1707","source":"Crossref","is-referenced-by-count":19,"title":["Geometry-based next frame prediction from monocular video"],"prefix":"10.1109","author":[{"given":"Reza","family":"Mahjourian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Wicke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anelia","family":"Angelova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.281"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.18"},{"journal-title":"Unsupervised learning of video representations using lstms","year":"2015","author":"srivastava","key":"ref31"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2016.7535373"},{"key":"ref34","article-title":"Probabilistic modeling of future frames from a single image","author":"xue","year":"2016","journal-title":"NIPS"},{"key":"ref10","article-title":"Map-based long term motion prediction for vehicles in traffic environments","author":"petrich","year":"2016","journal-title":"International Conference on Intelligent Transportation"},{"journal-title":"Tensorflow A system for large-scale machine learning","year":"2016","author":"abadi","key":"ref11"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298897"},{"key":"ref13","article-title":"Convolutional Istm network: A machine learning approach for precipitation nowcasting","author":"shi","year":"2015","journal-title":"NIPS"},{"key":"ref14","article-title":"Unsupervised learning for physical interaction through video prediction","author":"finn","year":"2016","journal-title":"NIPS"},{"journal-title":"Flownet Learning optical flow with convolutional networks","year":"2015","author":"fischer","key":"ref15"},{"key":"ref16","article-title":"Learning predictive visual models of physics for playing billiards","author":"fragkiadaki","year":"2016","journal-title":"ICLRE"},{"key":"ref17","article-title":"Unsupervised cnn for single view depth estimation: Geometry to the rescue","author":"garg","year":"2016","journal-title":"ECCV"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.185"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913491297"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-013-0639-7"},{"journal-title":"Video (language) modeling a baseline for generative models of natural videos","year":"2014","author":"ranzato","key":"ref28"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2005.1505111"},{"key":"ref27","article-title":"Pedestrian detection: An evaluation of the state of the art","author":"schiele","year":"2012","journal-title":"PAMI"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2012.6338748"},{"key":"ref29","article-title":"Monocular 3d shape reconstruction using deep neural networks","author":"rao","year":"2016","journal-title":"Intelligent Vehicles Symposium"},{"key":"ref5","article-title":"Ten years of pedestrian detection, what have we learned?","author":"benenson","year":"2014","journal-title":"Computer Vision for Road Scene Understanding and Autonomous Driving (CVRSUAD ECCV workshop)"},{"journal-title":"Single-image depth perception in the wild","year":"2016","author":"chen","key":"ref8"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.18"},{"journal-title":"Layer normalization","year":"2016","author":"ba","key":"ref2"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33786-4_28"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.304"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"journal-title":"Adam A method for stochastic optimization","year":"2014","author":"kingma","key":"ref22"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2010.579"},{"journal-title":"Deep multi-scale video prediction beyond mean square error","year":"2015","author":"mathieu","key":"ref24"},{"journal-title":"Deeper depth prediction with fully convolutional residual networks","year":"2016","author":"laina","key":"ref23"},{"key":"ref26","article-title":"Action-conditional video prediction using deep networks in atari games","author":"oh","year":"2015","journal-title":"NIPS"},{"key":"ref25","article-title":"Modeling deep temporal dependencies with recurrent grammar cells","author":"michalski","year":"2014","journal-title":"NIPS"}],"event":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","start":{"date-parts":[[2017,6,11]]},"location":"Los Angeles, CA, USA","end":{"date-parts":[[2017,6,14]]}},"container-title":["2017 IEEE Intelligent Vehicles Symposium (IV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7987634\/7995688\/07995953.pdf?arnumber=7995953","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,10,2]],"date-time":"2017-10-02T22:49:20Z","timestamp":1506984560000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7995953\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/ivs.2017.7995953","relation":{},"subject":[],"published":{"date-parts":[[2017,6]]}}}