Fetching the paper…
Reading the bibliography…
In this paper, we propose to exploit the rich hierarchical features of deep convolutional neural networks to improve the accuracy and robustness of visual tracking.
B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” in Proc. of Int. Joint Conf. on Artificial Intelligence , 1981
1981
Earlier work this paper cites.
A. C. Bovik, M. Clark, and W. S. Geisler, “Multichannel texture analysis using localized spatial filters,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 12, no. 1, pp. 55–73, 1990
1990
Earlier work this paper cites.
D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 25, no. 5, pp. 564–575, 2003
2003
Earlier work this paper cites.
S. Avidan, “Support vector tracking,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 26, no. 8, pp. 1064–1072, 2004
2004
Earlier work this paper cites.
I. Matthews, T. Ishikawa, and S. Baker, “The template update problem,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 26, no. 6, pp. 810–815, 2004
2004
Earlier work this paper cites.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. of Computer Vision , vol. 60, no. 2, pp. 91–110, 2004
2004
Earlier work this paper cites.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2005
2005
Earlier work this paper cites.
A. Yilmaz, O. Javed, and M. Shah, “Object tracking: A survey,” ACM Computing Surveys , vol. 38, no. 4, 2006
2006
Earlier work this paper cites.
S. Avidan, “Ensemble tracking,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 29, no. 2, pp. 261–271, 2007
2007
Earlier work this paper cites.
D. A. Ross, J. Lim, R.-S. Lin, and M.-H. Yang, “Incremental learning for robust visual tracking,” Int. J. of Computer Vision , vol. 77, no. 1-3, pp. 125–141, 2008
2008
Earlier work this paper cites.
H. Grabner, C. Leistner, and H. Bischof, “Semi-supervised on-line boosting for robust tracking,” in Proc. of European Conf. on Computer Vision , 2008
2008
Earlier work this paper cites.
A. Torralba, R. Fergus, and W. T. Freeman, “80 million tiny images: A large data set for nonparametric object and scene recognition,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 30, no. 11, pp. 1958–1970, 2008
2008
Earlier work this paper cites.
X. Mei and H. Ling, “Robust visual tracking using ℓ \ell 1 minimization,” in Proc. of IEEE Int. Conf. on Computer Vision , 2009
2009
Earlier work this paper cites.
D. Ta, W. Chen, N. Gelfand, and K. Pulli, “Surftrac: Efficient tracking and continuous object recognition using local feature descriptors,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and F. Li, “Imagenet: A large-scale hierarchical image database,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2009
2009
Earlier work this paper cites.
Q. Zhao, Z. Yang, and H. Tao, “Differential earth mover’s distance with its applications to visual tracking,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 32, no. 2, pp. 274–287, 2010
2010
Earlier work this paper cites.
D. S. Bolme, J. R. Beveridge, B. A. Draper, and Y. M. Lui, “Visual object tracking using adaptive correlation filters,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2010
2010
Earlier work this paper cites.
J. Fan, W. Xu, Y. Wu, and Y. Gong, “Human tracking using convolutional neural networks,” IEEE Trans. Neural Networks , vol. 21, no. 10, pp. 1610–1623, 2010
2010
Earlier work this paper cites.
B. Babenko, M.-H. Yang, and S. Belongie, “Robust object tracking with online multiple instance learning,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 33, no. 8, 2011
2011
Earlier work this paper cites.
E. Rublee, V. Rabaud, K. Konolige, and G. R. Bradski, “ORB: An efficient alternative to SIFT or SURF,” in Proc. of IEEE Int. Conf. on Computer Vision , 2011
2011
Earlier work this paper cites.
S. Wang, H. Lu, F. Yang, and M. Yang, “Superpixel tracking,” in Proc. of IEEE Int. Conf. on Computer Vision , 2011
2011
Earlier work this paper cites.
S. Hare, A. Saffari, and P. H. S. Torr, “Struck: Structured output tracking with kernels,” in Proc. of IEEE Int. Conf. on Computer Vision , 2011
2011
Earlier work this paper cites.
K. E. Van de Sande, J. R. Uijlings, T. Gevers, and A. W. Smeulders, “Segmentation as selective search for object recognition,” in Proc. of IEEE Int. Conf. on Computer Vision , 2011
2011
Earlier work this paper cites.
Z. Kalal, K. Mikolajczyk, and J. Matas, “Tracking-learning-detection,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 34, no. 7, pp. 1409–1422, 2012
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. of Advances in Neural Inf. Process. Systems , 2012
2012
Earlier work this paper cites.
J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, “Exploiting the circulant structure of tracking-by-detection with kernels,” in Proc. of European Conf. on Computer Vision , 2012
2012
Earlier work this paper cites.
F. Pernici, “Facehugger: The ALIEN tracker applied to faces,” in Proc. of European Conf. on Computer Vision , 2012
2012
Earlier work this paper cites.
W. Y. Zou, A. Y. Ng, S. Zhu, and K. Yu, “Deep learning of invariant features via simulated fixations in video,” in Proc. of Advances in Neural Inf. Process. Systems , 2012
2012
Cited alongside, same era.
X. Li, W. Hu, C. Shen, Z. Zhang, A. R. Dick, and A. van den Hengel, “A survey of appearance models in visual object tracking,” ACM Transactions on Intelligent Systems and Technology , vol. 4, no. 4, p. 58, 2013
2013
Cited alongside, same era.
N. Wang and D. Yeung, “Learning a deep compact image representation for visual tracking,” in Proc. of Advances in Neural Inf. Process. Systems , 2013
2013
Cited alongside, same era.
Y. Wu, J. Lim, and M.-H. Yang, “Online object tracking: A benchmark,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2013
2013
Cited alongside, same era.
Q. Bai, Z. Wu, S. Sclaroff, M. Betke, and C. Monnier, “Randomized ensemble tracking,” in Proc. of IEEE Int. Conf. on Computer Vision , 2013
——, “Object tracking benchmark,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 37, no. 9, pp. 1834–1848, 2015
2015
Later among the works it cites.
M. Kristan, J. Matas, A. Leonardis, M. Felsberg, L. Cehovin, G. Fernández, T. Vojír, G. Häger, G. Nebehay, and R. P. Pflugfelder, “The visual object tracking VOT2015 challenge results,” in Proc. of IEEE Int. Conf. on Computer Vision Workshop , 2015
2015
Later among the works it cites.
M. Danelljan, G. Häger, F. S. Khan, and M. Felsberg, “Learning spatially regularized correlation filters for visual tracking,” in Proc. of IEEE Int. Conf. on Computer Vision , 2015
2015
Later among the works it cites.
C. Ma, X. Yang, C. Zhang, and M. Yang, “Long-term correlation tracking,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2015
2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2013
Cited alongside, same era.
J. S. Supancic and D. Ramanan, “Self-paced learning for long-term tracking,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2013
2013
Cited alongside, same era.
H. K. Galoogahi, T. Sim, and S. Lucey, “Multi-channel correlation filters,” in Proc. of IEEE Int. Conf. on Computer Vision , 2013
2013
Cited alongside, same era.
V. N. Boddeti, T. Kanade, and B. V. K. V. Kumar, “Correlation filters for object alignment,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2013
2013
Cited alongside, same era.
A. W. M. Smeulders, D. M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, and M. Shah, “Visual tracking: An experimental survey,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 36, no. 7, pp. 1442–1468, 2014
2014
Cited alongside, same era.
R. B. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2014
2014
Cited alongside, same era.
H. Li, Y. Li, and F. Porikli, “Deeptrack: Learning discriminative feature representations by convolutional neural networks for visual tracking,” in Proc. of British Machine Vision Conf. , 2014
2014
Cited alongside, same era.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in Proc. of European Conf. on Computer Vision , 2014, pp. 818–833
2014
Cited alongside, same era.
2015
Later among the works it cites.
L. Wang, W. Ouyang, X. Wang, and H. Lu, “Visual tracking with fully convolutional networks,” in Proc. of IEEE Int. Conf. on Computer Vision , 2015
2015
Later among the works it cites.
A. Ghodrati, A. Diba, M. Pedersoli, T. Tuytelaars, and L. Van Gool, “Deepproposal: Hunting objects by cascading deep convolutional layers,” in Proc. of IEEE Int. Conf. on Computer Vision , 2015
2015
Later among the works it cites.
Y. Hua, K. Alahari, and C. Schmid, “Online object tracking with proposal selection,” in Proc. of IEEE Int. Conf. on Computer Vision , 2015
2015
Later among the works it cites.
D. Huang, L. Luo, M. Wen, Z. Chen, and C. Zhang, “Enable scale and aspect ratio adaptability in visual tracking with detection proposals,” in Proc. of British Machine Vision Conf. , 2015
2015
Later among the works it cites.
B. Hariharan, P. A. Arbeláez, R. B. Girshick, and J. Malik, “Hypercolumns for object segmentation and fine-grained localization,” Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2015
2015
Later among the works it cites.
C. Ma, J. Huang, X. Yang, and M. Yang, “Hierarchical convolutional features for visual tracking,” in Proc. of IEEE Int. Conf. on Computer Vision , 2015
2015
Later among the works it cites.
Z. Hong, Z. Chen, C. Wang, X. Mei, D. Prokhorov, and D. Tao, “Multi-store tracker (MUSTer): A cognitive psychology inspired approach to object tracking,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2015
2015
Later among the works it cites.
M. Kristan, J. Matas, A. Leonardis, T. Vojír, R. P. Pflugfelder, G. Fernández, G. Nebehay, F. Porikli, and L. Cehovin, “A novel performance evaluation methodology for single-target trackers,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 38, no. 11, pp. 2137–2155, 2016
2016
Later among the works it cites.
H. Nam and B. Han, “Learning multi-domain convolutional neural networks for visual tracking,” Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2016
2016
Later among the works it cites.
K. Zhang, Q. Liu, Y. Wu, and M. Yang, “Robust visual tracking via convolutional networks without training,” IEEE Trans. Image Process. , vol. 25, no. 4, pp. 1779–1792, 2016
2016
Later among the works it cites.
H. Nam and B. Han, “Learning multi-domain convolutional neural networks for visual tracking,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2016
2016
Later among the works it cites.
R. Tao, E. Gavves, and A. W. M. Smeulders, “Siamese instance search for tracking,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2016
2016
Later among the works it cites.
Y. Qi, S. Zhang, L. Qin, H. Yao, Q. Huang, J. Lim, and M. Yang, “Hedged deep tracking,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2016
2016
Later among the works it cites.
M. Danelljan, A. Robinson, F. S. Khan, and M. Felsberg, “Beyond correlation filters: Learning continuous convolution operators for visual tracking,” in Proc. of European Conf. on Computer Vision , 2016
2016
Later among the works it cites.
L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. S. Torr, “Fully-convolutional siamese networks for object tracking,” in Proc. of European Conf. on Computer Vision Workshop , 2016
2016
Later among the works it cites.
D. Held, S. Thrun, and S. Savarese, “Learning to track at 100 FPS with deep regression networks,” in Proc. of European Conf. on Computer Vision , 2016
2016
Later among the works it cites.
G. Zhu, F. Porikli, and H. Li, “Beyond local search: Tracking objects everywhere with instance-specific proposals,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2016
2016
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2016
2016
Later among the works it cites.
M. Danelljan, G. Bhat, F. S. Khan, and M. Felsberg, “ECO: Efficient Convolution Operators for Tracking,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition , 2017
2017
Closest in time.
C. Ma, J.-B. Huang, X. Yang, and M.-H. Yang, “Adaptive correlation filters with long-term and short-term memory for object tracking,” Int. J. of Computer Vision , pp. 1–26, 2018
2018
Closest in time.
M. D. Zeiler, G. W. Taylor, and R. Fergus, “Adaptive deconvolutional networks for mid and high level feature learning,” in Proc. of IEEE Int. Conf. on Computer Vision , 2011, pp. 2018–2025
2025
Closest in time.