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Across a majority of modern learning-based tracking systems, expensive annotations are needed to achieve state-of-the-art performance.
Lucas-kanade 20 years on: A unifying framework
S. Baker and I. Matthews · 2004
Earlier work this paper cites.
Distinctive image features from scale-invariant keypoints
D. G. Lowe · 2004
Earlier work this paper cites.
Orb: An efficient alternative to sift or surf
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski · 2011
Earlier work this paper cites.
Lsd-slam: Large-scale direct monocular slam
J. Engel, T. Schöps, and D. Cremers · 2014
Earlier work this paper cites.
Svo: Fast semi-direct monocular visual odometry
C. Forster, M. Pizzoli, and D. Scaramuzza · 2014
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Feature-based lucas–kanade and active appearance models
E. Antonakos, J. Alabort-i Medina, G. Tzimiropoulos, and S. P. Zafeiriou · 2015
Earlier work this paper cites.
Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
H. Nam and B. Han · 2015
Earlier work this paper cites.
Photometric bundle adjustment for vision-based slam
H. Alismail, B. Browning, and S. Lucey · 2016
Cited alongside, same era.
Robust tracking in low light and sudden illumination changes
H. Alismail, B. Browning, and S. Lucey · 2016
Cited alongside, same era.
In defense of gradient-based alignment on densely sampled sparse features
H. Bristow and S. Lucey · 2016
Cited alongside, same era.
ECO: Efficient Convolution Operators for Tracking
M. Danelljan, G. Bhat, F. S. Khan, and M. Felsberg · 2016
Cited alongside, same era.
Beyond correlation filters: Learning continuous convolution operators for visual tracking
M. Danelljan, A. Robinson, F. S. Khan, and M. Felsberg · 2016
Cited alongside, same era.
Learning to track at 100 fps with deep regression networks
D. Held, S. Thrun, and S. Savarese · 2016
Visual tracking with fully convolutional networks
L. Wang, W. Ouyang, X. Wang, and H. Lu · 2016
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Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking
L. Č. Zajc, A. Lukežič, A. Leonardis, and M. Kristan · 2016
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CLKN: Cascaded Lucas-Kanade Networks for Image Alignment
C.-N. Chang, C.-N. Chou, and E. Chang · 2017
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Parallel Tracking and Verifying: A Framework for Real-Time and High Accuracy Visual Tracking
H. Fan and H. Ling · 2017
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Detect to Track and Track to Detect
C. Feichtenhofer, A. Pinz, and A. Zisserman · 2017
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End-to-end representation learning for Correlation Filter based tracking
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Cited alongside, same era.
Inverse Compositional Spatial Transformer Networks
C.-H. Lin and S. Lucey · 2016
Cited alongside, same era.
https://github.com/pytorch/pytorch
Pytorch: Tensors and dynamic neural networks in python with strong gpu acceleration
Cited in the paper.
J. Valmadre, L. Bertinetto, J. F. Henriques, A. Vedaldi, and P. H. S. Torr · 2017
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Deep-LK for Efficient Adaptive Object Tracking
C. Wang, H. K. Galoogahi, C.-H. Lin, and S. Lucey · 2017
Later among the works it cites.