Fetching the paper…
Reading the bibliography…
The majority of contemporary object-tracking approaches do not model interactions between objects.
Global data association for multi-object tracking using network flows
Li Zhang, Yuan Li, and Ramakant Nevatia · 2008
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jagannath Malik · 2014
Earlier work this paper cites.
Continuous energy minimization for multitarget tracking
Anton Milan, Stefan Roth, and Konrad Schindler · 2014
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Girshick Ross, and Jian Sun · 2015
Earlier work this paper cites.
DETRAC: A new benchmark and protocol for multi-object tracking
Longyin Wen, Dawei Du, Zhaowei Cai, Zhen Lei, Ming-Ching Chang, Honggang Qi, Jongwoo Lim, Ming-Hsuan Yang, and Siwei Lyu · 2015
Earlier work this paper cites.
Social LSTM: Human trajectory prediction in crowded spaces
Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese · 2016
Earlier work this paper cites.
Learning to track at 100 fps with deep regression networks
David Held, Sebastian Thrun, and Silvio Savarese · 2016
Earlier work this paper cites.
MOT16: A benchmark for multi-object tracking
A. Milan, L. Leal-Taixé, I. Reid, S. Roth, and K. Schindler · 2016
Earlier work this paper cites.
Learning social etiquette: Human trajectory prediction in crowded scenes
A. Robicquet, A. Sadeghian, A. Alahi, and S. Savaresei · 2016
Earlier work this paper cites.
Crowd scene understanding with coherent recurrent neural networks
Hang Su, Yinpeng Dong, Jun Zhu, Haibin Ling, and Bo Zhang · 2016
Earlier work this paper cites.
Confidence-based data association and discriminative deep appearance learning for robust online multi-object tracking
Seung-Hwan Bae and Kuk-Jin Yoon · 2017
Earlier work this paper cites.
ECO: efficient convolution operators for tracking
Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, and Michael Felsberg · 2017
Earlier work this paper cites.
RATM: recurrent attentive tracking model
Samira Ebrahimi Kahou, Vincent Michalski, and Roland Memisevic · 2017
Cited alongside, same era.
Hierarchical attentive recurrent tracking
Adam R. Kosiorek, Alex Bewley, and Ingmar Posner · 2017
Cited alongside, same era.
End-to-end representation learning for correlation filter based tracking
Jack Valmadre, Luca Bertinetto, João F. Henriques, Andrea Vedaldi, and Philip Hilaire Sean Torr · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Deep Sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbhakhsh, Barnabás Póczos, Ruslan Salakhutdinov, and Alexander Smola · 2017
Cited alongside, same era.
Soft+ hardwired attention: An lstm framework for human trajectory prediction and abnormal event detection
Tharindu Fernando, Simon Denman, Sridha Sridharan, and Clinton Fookes · 2018
Relational neural expectation maximization: Unsupervised discovery of objects and their interactions
Sjoerd van Steenkiste, Michael Chang, Klaus Greff, and Jürgen Schmidhuber · 2018
Later among the works it cites.
Learning discriminative model prediction for tracking
G. Bhat, M. Danelljan, L. Van Gool, and R. Timofte · 2019
Closest in time.
Set transformer
Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R Kosiorek, Seungjin Choi, and Yee Whye Teh · 2019
Closest in time.
Siamrpn++: Evolution of siamese visual tracking with very deep networks
B. Li, W. Wu, Q. Wang, F. Zhang, J. Xing, and J. Yan · 2019
Closest in time.
Deep attention models for human tracking using rgbd
Maryam Rasoulidanesh, Srishti Yadav, Sachini Herath, Yasaman Vaghei, and Shahram Payandeh · 2019
Closest in time.
Sophie: An attentive gan for predicting paths compliant to social and physical constraints
Amir Sadeghian, Vineet Kosaraju, Ali Sadeghian, Noriaki Hirose, Hamid Rezatofighi, and Silvio Savarese · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
End-to-end learning of multi-sensor 3d tracking by detection
Davi Frossard and Raquel Urtasun · 2018
Cited alongside, same era.
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
Daniel Gordon, Ali Farhadi, and Dieter Fox · 2018
Cited alongside, same era.
Social GAN: socially acceptable trajectories with generative adversarial networks
Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, and Alexandre Alahi · 2018
Cited alongside, same era.
Motion segmentation & multiple object tracking by correlation co-clustering
Margret Keuper, Siyu Tang, Bjorn Andres, Thomas Brox, and Bernt Schiele · 2018
Cited alongside, same era.
Sequential attend, infer, repeat: Generative modelling of moving objects
Adam Kosiorek, Hyunjik Kim, Yee Whye Teh, and Ingmar Posner · 2018
Cited alongside, same era.
Closest in time.
On the limitations of representing functions on sets
Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Ingmar Posner, and Michael A. Osborne · 2019
Closest in time.
Scalor: Generative world models with scalable object representations
Jindong Jiang, Sepehr Janghorbani, Gerard de Melo, and Sungjin Ahn · 2020
Closest in time.
Siam r-cnn: Visual tracking by re-detection
Paul Voigtlaender, Jonathon Luiten, Philip Torr, and Bastian Leibe · 2020
Closest in time.
A simple baseline for multi-object tracking
Yifu Zhang, Chunyu Wang, Xinggang Wang, Wen-Jun Zeng, and Wen-Yu Liu · 2020
Closest in time.
Xingyi Zhou, Vladlen Koltun, and Philipp Krähenbühl · 2020
Closest in time.