Fetching the paper…
Reading the bibliography…
Deep networks have been successfully applied to visual tracking by learning a generic representation offline from numerous training images.
D. H. Hubel and T. N. Wiesel, “Receptive fields of single neurones in the cat’s striate cortex,” The Journal of physiology
1959
Earlier work this paper cites.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural computation
1989
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE
1998
Earlier work this paper cites.
M. Riesenhuber and T. Poggio, “Hierarchical models of object recognition in cortex,” Nature neuroscience
1999
Earlier work this paper cites.
S. Ben-Yacoub, B. Fasel, and J. Luettin, “Fast face detection using mlp and fft,” in AVBPA
1999
Earlier work this paper cites.
P. Pérez, C. Hue, J. Vermaak, and M. Gangnet, “Color-based probabilistic tracking,” in Proceedings of European Conference on Computer Vision
2002
Earlier work this paper cites.
D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2003
Earlier work this paper cites.
S. Avidan, “Support vector tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2004
Earlier work this paper cites.
S. Baker and I. Matthews, “Lucas-kanade 20 years on: A unifying framework,” International Journal of Computer Vision
2004
Earlier work this paper cites.
I. Matthews, T. Ishikawa, and S. Baker, “The template update problem,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2004
Earlier work this paper cites.
R. Collins, X. Zhou, and S. K. Teh, “An open source tracking testbed and evaluation web site,” in PETS
2005
Earlier work this paper cites.
A. Adam, E. Rivlin, and I. Shimshoni, “Robust fragments-based tracking using the integral histogram,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
2006
Earlier work this paper cites.
H. Grabner, M. Grabner, and H. Bischof, “Real-time tracking via on-line boosting,” in Proceedings of British Machine Vision Conference
2006
Earlier work this paper cites.
T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio, “Robust object recognition with cortex-like mechanisms,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2007
Earlier work this paper cites.
D. A. Ross, J. Lim, R.-S. Lin, and M.-H. Yang, “Incremental learning for robust visual tracking,” International Journal of Computer Vision
2008
Earlier work this paper cites.
H. Grabner, C. Leistner, and H. Bischof, “Semi-supervised on-line boosting for robust tracking,” in Proceedings of European Conference on Computer Vision
2008
Earlier work this paper cites.
J. Kwon and K. M. Lee, “Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive basin hopping monte carlo sampling,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
2009
Earlier work this paper cites.
J. Kwon and K. M. Lee, “Visual tracking decomposition.,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
2010
Cited alongside, same era.
Z. Kalal, J. Matas, and K. Mikolajczyk, “Pn learning: Bootstrapping binary classifiers by structural constraints,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
2010
Cited alongside, same era.
J. Fan, W. Xu, Y. Wu, and Y. Gong, “Human tracking using convolutional neural networks,” IEEE Transactions on Neural Networks
2010
Cited alongside, same era.
Y.-L. Boureau, F. Bach, Y. LeCun, and J. Ponce, “Learning mid-level features for recognition,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
2010
Cited alongside, same era.
M. Elad, M. A. Figueiredo, and Y. Ma, “On the role of sparse and redundant representations in image processing,” Proceedings of the IEEE
C. Bao, Y. Wu, H. Ling, and H. Ji, “Real time robust l1 tracker using accelerated proximal gradient approach,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
2012
Later among the works it cites.
T. Zhang, B. Ghanem, S. Liu, and N. Ahuja, “Robust visual tracking via multi-task sparse learning,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
2012
Later among the works it cites.
J. Henriques, R. Caseiro, P. Martins, and J. Batista, “Exploiting the circulant structure of tracking-by-detection with kernels,” in Proceedings of European Conference on Computer Vision
2012
Later among the works it cites.
L. Sevilla-Lara and E. Learned-Miller, “Distribution fields for tracking,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
2012
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2010
Cited alongside, same era.
S. Hare, A. Saffari, and P. H. Torr, “Struck: Structured output tracking with kernels,” in Proceedings of the IEEE International Conference on Computer Vision
2011
Cited alongside, same era.
T. B. Dinh, N. Vo, and G. Medioni, “Context tracker: Exploring supporters and distracters in unconstrained environments,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
2011
Cited alongside, same era.
A. Coates, A. Y. Ng, and H. Lee, “An analysis of single-layer networks in unsupervised feature learning,” in Advances in Neural Information Processing Systems
2011
Cited alongside, same era.
X. Mei and H. Ling, “Robust visual tracking and vehicle classification via sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2011
Cited alongside, same era.
B. Liu, J. Huang, L. Yang, and C. Kulikowsk, “Robust tracking using local sparse appearance model and k-selection,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
2011
Cited alongside, same era.
B. Babenko, M.-H. Yang, and S. Belongie, “Robust object tracking with online multiple instance learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2011
Cited alongside, same era.
J. Kwon and K. M. Lee, “Tracking by sampling trackers,” in Proceedings of the IEEE International Conference on Computer Vision
2011
Cited alongside, same era.
S. Oron, A. Bar-Hillel, D. Levi, and S. Avidan, “Locally orderless tracking,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
2012
Later among the works it cites.
N. Wang and D.-Y. Yeung, “Learning a deep compact image representation for visual tracking,” in Advances in Neural Information Processing Systems
2013
Later among the works it cites.
Y. Wu, J. Lim, and M.-H. Yang, “Online object tracking: A benchmark,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
2013
Later among the works it cites.
X. Li, W. Hu, C. Shen, Z. Zhang, A. Dick, and A. V. D. Hengel, “A survey of appearance models in visual object tracking,” ACM Transactions on Intelligent Systems and Technology
2013
Later among the works it cites.
J. Gao, H. Ling, W. Hu, and J. Xing, “Transfer learning based visual tracking with gaussian processes regression,” in Proceedings of European Conference on Computer Vision
2014
Later among the works it cites.
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell, “Decaf: A deep convolutional activation feature for generic visual recognition,” in International Conference on Machine Learning
2014
Later among the works it cites.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
2014
Later among the works it cites.
H. Li, Y. Li, and F. Porikli, “Robust online visual tracking with a single convolutional neural network,” in Proceedings of Asian Conference on Computer Vision
2014
Later among the works it cites.
X. Zhou, L. Xie, P. Zhang, and Y. Zhang, “An ensemble of deep neural networks for object tracking,” in IEEE International Conference on Image Processing
2014
Later among the works it cites.
K. Zhang, L. Zhang, Q. Liu, D. Zhang, and M.-H. Yang, “Fast visual tracking via dense spatio-temporal context learning,” in Proceedings of European Conference on Computer Vision
2014
Later among the works it cites.
J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, “High-speed tracking with kernelized correlation filters,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2015
Closest in time.
L. Wang, T. Liu, G. Wang, K. L. Chan, and Q. Yang, “Video tracking using learned hierarchical features,” IEEE Transactions on Image Processing
2015
Closest in time.