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Monocular object detection and tracking have improved drastically in recent years, but rely on a key assumption: that objects are visible to the camera.
Determining the movement of objects from a sequence of images
John W Roach and JK Aggarwal · 1980
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Recursive 3-d motion estimation from a monocular image sequence
Ted J Broida, S Chandrashekhar, and Rama Chellappa · 1990
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Object permanence in young infants: Further evidence
Renée Baillargeon and Julie DeVos · 1991
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A multiple-baseline stereo
Masatoshi Okutomi and Takeo Kanade · 1991
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Tracking multiple objects through occlusions
Yan Huang and Irfan Essa · 2005
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A multiview approach to tracking people in crowded scenes using a planar homography constraint
Saad M Khan and Mubarak Shah · 2006
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Multi-camera tracking and segmentation of occluded people on ground plane using search-guided particle filtering
Kyungnam Kim and Larry S Davis · 2006
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Multicamera people tracking with a probabilistic occupancy map
Francois Fleuret, Jerome Berclaz, Richard Lengagne, and Pascal Fua · 2007
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Crowds by example
Alon Lerner, Yiorgos Chrysanthou, and Dani Lischinski · 2007
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Multiple target tracking using spatio-temporal markov chain monte carlo data association
Qian Yu, Gérard Medioni, and Isaac Cohen · 2007
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Evaluating multiple object tracking performance: the clear mot metrics
Keni Bernardin and Rainer Stiefelhagen · 2008
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Inverse depth parametrization for monocular slam
Javier Civera, Andrew J Davison, and JM Martinez Montiel · 2008
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Parametric image alignment using enhanced correlation coefficient maximization
Georgios D Evangelidis and Emmanouil Z Psarakis · 2008
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Object detection with discriminatively trained part-based models
Pedro F Felzenszwalb, Ross B Girshick, David McAllester, and Deva Ramanan · 2009
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You’ll never walk alone: Modeling social behavior for multi-target tracking
Stefano Pellegrini, Andreas Ess, Konrad Schindler, and Luc Van Gool · 2009
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Learning pedestrian dynamics from the real world
Paul Scovanner and Marshall F Tappen · 2009
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The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2010
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Tracking the invisible: Learning where the object might be
Helmut Grabner, Jiri Matas, Luc Van Gool, and Philippe Cattin · 2010
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Nonlinear estimation with state-dependent gaussian observation noise
Davide Spinello and Daniel J Stilwell · 2010
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Multiple object tracking using k-shortest paths optimization
Jerome Berclaz, Francois Fleuret, Engin Turetken, and Pascal Fua · 2011
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Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker
Laura Leal-Taixé, Gerard Pons-Moll, and Bodo Rosenhahn · 2011
Cited alongside, same era.
Globally-optimal greedy algorithms for tracking a variable number of objects
Hamed Pirsiavash, Deva Ramanan, and Charless C Fowlkes · 2011
Cited alongside, same era.
Who are you with and where are you going?
Kota Yamaguchi, Alexander C Berg, Luis E Ortiz, and Tamara L Berg · 2011
Cited alongside, same era.
Activity forecasting
Kris M Kitani, Brian D Ziebart, James Andrew Bagnell, and Martial Hebert · 2012
Cited alongside, same era.
Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
Cited alongside, same era.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Social gan: Socially acceptable trajectories with generative adversarial networks
Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, and Alexandre Alahi · 2018
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Megadepth: Learning single-view depth prediction from internet photos
Zhengqi Li and Noah Snavely · 2018
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Deep cosine metric learning for person re-identification
Nicolai Wojke and Alex Bewley · 2018
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Youtube-vos: A large-scale video object segmentation benchmark
Ning Xu, Linjie Yang, Yuchen Fan, Dingcheng Yue, Yuchen Liang, Jianchao Yang, and Thomas Huang · 2018
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Tracking without bells and whistles
Philipp Bergmann, Tim Meinhardt, and Laura Leal-Taixe · 2019
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Argoverse: 3d tracking and forecasting with rich maps
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Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Cited alongside, same era.
Yan Zhu, Yuandong Tian, Dimitris Mexatas, and Piotr Dollár · 2015
Cited alongside, same era.
Social lstm: Human trajectory prediction in crowded spaces
Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese · 2016
Cited alongside, same era.
Simple online and realtime tracking
Alex Bewley, Zongyuan Ge, Lionel Ott, Fabio Ramos, and Ben Upcroft · 2016
Cited alongside, same era.
Amodal instance segmentation
Ke Li and Jitendra Malik · 2016
Cited alongside, same era.
Ming-Fang Chang, John Lambert, Patsorn Sangkloy, Jagjeet Singh, Slawomir Bak, Andrew Hartnett, De Wang, Peter Carr, Simon Lucey, Deva Ramanan, et al · 2019
Later among the works it cites.
Stgat: Modeling spatial-temporal interactions for human trajectory prediction
Yingfan Huang, HuiKun Bi, Zhaoxin Li, Tianlu Mao, and Zhaoqi Wang · 2019
Later among the works it cites.
Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
Katrin Lasinger, René Ranftl, Konrad Schindler, and Vladlen Koltun · 2019
Later among the works it cites.
Learning the depths of moving people by watching frozen people
Zhengqi Li, Tali Dekel, Forrester Cole, Richard Tucker, Noah Snavely, Ce Liu, and William T Freeman · 2019
Later among the works it cites.
Amodal instance segmentation with KINS dataset
Lu Qi, Li Jiang, Shu Liu, Xiaoyong Shen, and Jiaya Jia · 2019
Later among the works it cites.
Visualizing the invisible: Occluded vehicle segmentation and recovery
Xiaosheng Yan, Feigege Wang, Wenxi Liu, Yuanlong Yu, Shengfeng He, and Jia Pan · 2019
Later among the works it cites.
Learning semantics-aware distance map with semantics layering network for amodal instance segmentation
Ziheng Zhang, Anpei Chen, Ling Xie, Jingyi Yu, and Shenghua Gao · 2019
Later among the works it cites.
Mot20: A benchmark for multi object tracking in crowded scenes
Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, and Laura Leal-Taixé · 2020
Closest in time.
Refinements in motion and appearance for online multi-object tracking
Piao Huang, Shoudong Han, Jun Zhao, Donghaisheng Liu, Hongwei Wang, En Yu, and Alex ChiChung Kot · 2020
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Simple unsupervised multi-object tracking
Shyamgopal Karthik, Ameya Prabhu, and Vineet Gandhi · 2020
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Gsm: Graph similarity model for multi-object tracking
Qiankun Liu, Qi Chu, Bin Liu, and Nenghai Yu · 2020
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Gcnnmatch: Graph convolutional neural networks for multi-object tracking via sinkhorn normalization
Ioannis Papakis, Abhijit Sarkar, and Anuj Karpatne · 2020
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Panda: A gigapixel-level human-centric video dataset
Xueyang Wang, Xiya Zhang, Yinheng Zhu, Yuchen Guo, Xiaoyun Yuan, Liuyu Xiang, Zerun Wang, Guiguang Ding, David Brady, Qionghai Dai, et al · 2020
Closest in time.
Xingyi Zhou, Vladlen Koltun, and Philipp Krähenbühl · 2020
Closest in time.