Fetching the paper…
Reading the bibliography…
Tracking by detection, the dominant approach for online multi-object tracking, alternates between localization and association steps.
Object permanence in five-month-old infants
Renee Baillargeon, Elizabeth S Spelke, and Stanley Wasserman · 1985
Earlier work this paper cites.
Principles of object perception
Elizabeth S Spelke · 1990
Earlier work this paper cites.
Backpropagation through time: what it does and how to do it
Paul J Werbos · 1990
Earlier work this paper cites.
Tracking multiple objects through occlusions
Yan Huang and Irfan Essa · 2005
Earlier work this paper cites.
Robust people tracking with global trajectory optimization
Jerome Berclaz, Francois Fleuret, and Pascal Fua · 2006
Earlier work this paper cites.
A linear programming approach for multiple object tracking
Hao Jiang, Sidney Fels, and James J Little · 2007
Earlier work this paper cites.
Multiple target tracking using spatio-temporal markov chain monte carlo data association
Qian Yu, Gérard Medioni, and Isaac Cohen · 2007
Earlier work this paper cites.
Evaluating multiple object tracking performance: the clear mot metrics
Keni Bernardin and Rainer Stiefelhagen · 2008
Earlier work this paper cites.
Global data association for multi-object tracking using network flows
Li Zhang, Yuan Li, and Ramakant Nevatia · 2008
Earlier work this paper cites.
Robust tracking-by-detection using a detector confidence particle filter
Michael D Breitenstein, Fabian Reichlin, Bastian Leibe, Esther Koller-Meier, and Luc Van Gool · 2009
Earlier work this paper cites.
Learning to associate: Hybridboosted multi-target tracker for crowded scene
Yuan Li, Chang Huang, and Ram Nevatia · 2009
Earlier work this paper cites.
Multiple target tracking in world coordinate with single, minimally calibrated camera
Wongun Choi and Silvio Savarese · 2010
Earlier work this paper cites.
Tracking the invisible: Learning where the object might be
Helmut Grabner, Jiri Matas, Luc Van Gool, and Philippe Cattin · 2010
Earlier work this paper cites.
Multi-person tracking with sparse detection and continuous segmentation
Dennis Mitzel, Esther Horbert, Andreas Ess, and Bastian Leibe · 2010
Earlier work this paper cites.
Multiple objects tracking in the presence of long-term occlusions
Vasilis Papadourakis and Antonis Argyros · 2010
Earlier work this paper cites.
Multiple object tracking using k-shortest paths optimization
Jerome Berclaz, Francois Fleuret, Engin Turetken, and Pascal Fua · 2011
Earlier work this paper cites.
Globally-optimal greedy algorithms for tracking a variable number of objects
Hamed Pirsiavash, Deva Ramanan, and Charless C Fowlkes · 2011
Earlier work this paper cites.
Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
Learning an image-based motion context for multiple people tracking
Laura Leal-Taixé, Michele Fenzi, Alina Kuznetsova, Bodo Rosenhahn, and Silvio Savarese · 2014
Earlier work this paper cites.
Near-online multi-target tracking with aggregated local flow descriptor
Wongun Choi · 2015
Earlier work this paper cites.
Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox · 2015
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
Earlier work this paper cites.
Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
Earlier work this paper cites.
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.
Learning to track: Online multi- object tracking by decision making
Yu Xiang, Alexandre Alahi, and Silvio Savarese · 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.
Delving deeper into convolutional networks for learning video representations
Nicolas Ballas, Li Yao, Chris Pal, and Aaron Courville · 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.
Virtual worlds as proxy for multi-object tracking analysis
Tracking without bells and whistles
Philipp Bergmann, Tim Meinhardt, and Laura Leal-Taixe · 2019
Later among the works it cites.
Generating human action videos by coupling 3D game engines and probabilistic graphical models
César Roberto de Souza, Adrien Gaidon, Yohann Cabon, Naila Murray, and Antonio Manuel López · 2019
Later among the works it cites.
Multiple object tracking with attention to appearance, structure, motion and size
Hasith Karunasekera, Han Wang, and Handuo Zhang · 2019
Later among the works it cites.
SPIGAN: privileged adversarial learning from simulation
Kuan-Hui Lee, German Ros, Jie Li, and Adrien Gaidon · 2019
Later among the works it cites.
Learning to segment moving objects
Pavel Tokmakov, Cordelia Schmid, and Karteek Alahari · 2019
Later among the works it cites.
DADA: Depth-aware domain adaptation in semantic segmentation
Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Mathieu Cord, and Patrick Pérez · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Adrien Gaidon, Qiao Wang, Yohann Cabon, and Eleonora Vig · 2016
Cited alongside, same era.
MOT16: A benchmark for multi-object tracking
Anton Milan, Laura Leal-Taixé, Ian Reid, Stefan Roth, and Konrad Schindler · 2016
Cited alongside, same era.
Playing for data: Ground truth from computer games
Stephan R Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun · 2016
Cited alongside, same era.
The Synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M Lopez · 2016
Cited alongside, same era.
Detect to track and track to detect
Christoph Feichtenhofer, Axel Pinz, and Andrew Zisserman · 2017
Cited alongside, same era.
FlowNet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2017
Cited alongside, same era.
Object detection in videos with tubelet proposal networks
Kai Kang, Hongsheng Li, Tong Xiao, Wanli Ouyang, Junjie Yan, Xihui Liu, and Xiaogang Wang · 2017
Cited alongside, same era.
Later among the works it cites.
A baseline for 3D multi-object tracking
Xinshuo Weng and Kris Kitani · 2019
Later among the works it cites.
Spatial-temporal relation networks for multi-object tracking
Jiarui Xu, Yue Cao, Zheng Zhang, and Han Hu · 2019
Later among the works it cites.
Video instance segmentation
Linjie Yang, Yuchen Fan, and Ning Xu · 2019
Later among the works it cites.
Xingyi Zhou, Dequan Wang, and Philipp Krähenbühl · 2019
Later among the works it cites.
Confidence regularized self-training
Yang Zou, Zhiding Yu, Xiaofeng Liu, B.V.K. Vijaya Kumar, and Jinsong Wang · 2019
Later among the works it cites.
Learning a neural solver for multiple object tracking
Guillem Brasó and Laura Leal-Taixé · 2020
Later among the works it cites.
nuScenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
Later among the works it cites.
TAO: A large-scale benchmark for tracking any object
Achal Dave, Tarasha Khurana, Pavel Tokmakov, Cordelia Schmid, and Deva Ramanan · 2020
Later among the works it cites.
SMAT: Smart multiple affinity metrics for multiple object tracking
Nicolas Franco Gonzalez, Andres Ospina, and Philippe Calvez · 2020
Later among the works it cites.
Instance adaptive self-training for unsupervised domain adaptation
Jiaqi Zou Ke Mei, Chuang Zhu and Shanghang Zhang · 2020
Later among the works it cites.
HOTA: A higher order metric for evaluating multi-object tracking
Jonathon Luiten, Aljosa Osep, Patrick Dendorfer, Philip Torr, Andreas Geiger, Laura Leal-Taixé, and Bastian Leibe · 2020
Later among the works it cites.
Esl: Entropy-guided self-supervised learning for domain adaptation in semantic segmentation
Antoine Saporta, Tuan-Hung Vu, M. Cord, and P. Pérez · 2020
Later among the works it cites.
Learning object permanence from video
Aviv Shamsian, Ofri Kleinfeld, Amir Globerson, and Gal Chechik · 2020
Later among the works it cites.
Tracking objects as points
Xingyi Zhou, Vladlen Koltun, and Philipp Krähenbühl · 2020
Later among the works it cites.
http://www.cvlibs.net/datasets/kitti/eval_tracking_detail.php?result=82c08bddb89f9faa0fb00a60d55fea792ebede7d , March 2021
KITTI benchmark · 2021
Closest in time.
https://motchallenge.net/method/MOT=4308&chl=10 , March 2021
MOT17 benchmark · 2021
Closest in time.
https://paralleldomain.com/ , March 2021
Parallel domain · 2021
Closest in time.
TrackMPNN: A message passing graph neural architecture for multi-object tracking
Akshay Rangesh, Pranav Maheshwari, Mez Gebre, Siddhesh Mhatre, Vahid Ramezani, and Mohan M Trivedi · 2021
Closest in time.