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Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, its motion, and how it changes over time.
Using boosted features for the detection of people in 2d range data
K. O. Arras, O. M. Mozos, and W. Burgard · 2007
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A lidar and vision-based approach for pedestrian and vehicle detection and tracking
C. Premebida, G. Monteiro, U. Nunes, and P. Peixoto · 2007
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Visualizing data using t-sne
L. v. d. Maaten and G. Hinton · 2008
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Are we ready for autonomous driving? the kitti vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
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Robust scale-adaptive mean-shift for tracking
T. Vojir, J. Noskova, and J. Matas · 2013
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Online object tracking: A benchmark
Y. Wu, J. Lim, and M.-H. Yang · 2013
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Accurate scale estimation for robust visual tracking
M. Danelljan, G. Häger, F. Khan, and M. Felsberg · 2014
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The matrioska tracking algorithm on ltdt2014 dataset
M. Edoardo Maresca and A. Petrosino · 2014
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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The visual object tracking vot2014 challenge results
M. Kristan et al · 2014
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Clustering local motion estimates for robust and efficient object tracking
M. E. Maresca and A. Petrosino · 2014
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Dart: Dense articulated real-time tracking
T. Schmidt, R. A. Newcombe, and D. Fox · 2014
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Visual tracking: An experimental survey
A. W. Smeulders, D. M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, and M. Shah · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Meem: robust tracking via multiple experts using entropy minimization
J. Zhang, S. Ma, and S. Sclaroff · 2014
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi et al · 2015
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Scheduled sampling for sequence prediction with recurrent neural networks
S. Bengio, O. Vinyals, N. Jaitly, and N. Shazeer · 2015
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Convolutional features for correlation filter based visual tracking
M. Danelljan, G. Hager, F. Shahbaz Khan, and M. Felsberg · 2015
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Recurrent network models for human dynamics
K. Fragkiadaki, S. Levine, P. Felsen, and J. Malik · 2015
Cited alongside, same era.
First step toward model-free, anonymous object tracking with recurrent neural networks
Fully-convolutional siamese networks for object tracking
L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. Torr · 2016
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Beyond correlation filters: Learning continuous convolution operators for visual tracking
M. Danelljan, A. Robinson, F. S. Khan, and M. Felsberg · 2016
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Lstm: A search space odyssey
K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber · 2016
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Struck: Structured output tracking with kernels
S. Hare, S. Golodetz, A. Saffari, V. Vineet, M.-M. Cheng, S. L. Hicks, and P. H. Torr · 2016
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Learning to track at 100 fps with deep regression networks
D. Held, S. Thrun, and S. Savarese · 2016
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The visual object tracking vot2016 challenge results
M. Kristan et al · 2016
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Q. Gan, Q. Guo, Z. Zhang, and K. Cho · 2015
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
High-speed tracking with kernelized correlation filters
J. F. Henriques, R. Caseiro, P. Martins, and J. Batista · 2015
Cited alongside, same era.
Multi-store tracker (muster): A cognitive psychology inspired approach to object tracking
Z. Hong, Z. Chen, C. Wang, X. Mei, D. Prokhorov, and D. Tao · 2015
Cited alongside, same era.
The visual object tracking vot2015 challenge results
M. Kristan et al · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
O. Russakovsky et al · 2015
Cited alongside, same era.
Scalable kernel correlation filter with sparse feature integration
A. Solis Montero, J. Lang, and R. Laganiere · 2015
Cited alongside, same era.
Learning multi-domain convolutional neural networks for visual tracking
H. Nam and B. Han · 2016
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Deep tracking: Seeing beyond seeing using recurrent neural networks
P. Ondruska and I. Posner · 2016
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Playing for data: Ground truth from computer games
S. R. Richter, V. Vineet, S. Roth, and V. Koltun · 2016
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Discriminative scale space tracking
M. Danelljan, G. Häger, F. S. Khan, and M. Felsberg · 2017
Closest in time.
Ratm: Recurrent attentive tracking model
S. Ebrahimi Kahou, V. Michalski, R. Memisevic, C. Pal, and P. Vincent · 2017
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T-cnn: Tubelets with convolutional neural networks for object detection from videos
K. Kang et al · 2017
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Xpose: Reinventing user interaction with flying cameras
Z. Lan, M. Shridhar, D. Hsu, and S. Zhao · 2017
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