-
1
-
-
79959527478
-
Robust object tracking with online multiple instance learning
-
B. Babenko, M.-H. Yang, and S. Belongie. Robust object tracking with online multiple instance learning. TPAMI, 33(8), 2011.
-
(2011)
TPAMI
, vol.33
, Issue.8
-
-
Babenko, B.1
Yang, M.-H.2
Belongie, S.3
-
4
-
-
0025263482
-
Multichannel texture analysis using localized spatial filters
-
A. C. Bovik, M. Clark, and W. S. Geisler. Multichannel texture analysis using localized spatial filters. TPAMI, 12(1):55-73, 1990.
-
(1990)
TPAMI
, vol.12
, Issue.1
, pp. 55-73
-
-
Bovik, A.C.1
Clark, M.2
Geisler, W.S.3
-
5
-
-
33645146449
-
Histograms of oriented gradients for human detection
-
N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
-
(2005)
CVPR
-
-
Dalal, N.1
Triggs, B.2
-
8
-
-
85198028989
-
Imagenet: A large-scale hierarchical image database
-
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and F. Li. Imagenet: A large-scale hierarchical image database. In CVPR, 2009.
-
(2009)
CVPR
-
-
Deng, J.1
Dong, W.2
Socher, R.3
Li, L.4
Li, K.5
Li, F.6
-
10
-
-
84956699399
-
Transfer learning based visual tracking with Gaussian processes regression
-
J. Gao, H. Ling, W. Hu, and J. Xing. Transfer learning based visual tracking with Gaussian processes regression. In ECCV, 2014.
-
(2014)
ECCV
-
-
Gao, J.1
Ling, H.2
Hu, W.3
Xing, J.4
-
11
-
-
84911400494
-
Rich feature hierarchies for accurate object detection and semantic segmentation
-
R. B. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
-
(2014)
CVPR
-
-
Girshick, R.B.1
Donahue, J.2
Darrell, T.3
Malik, J.4
-
12
-
-
70350531007
-
Semi-supervised on-line boosting for robust tracking
-
H. Grabner, C. Leistner, and H. Bischof. Semi-supervised on-line boosting for robust tracking. In ECCV, 2008.
-
(2008)
ECCV
-
-
Grabner, H.1
Leistner, C.2
Bischof, H.3
-
13
-
-
84856659290
-
Struck: Structured output tracking with kernels
-
S. Hare, A. Saffari, and P. H. S. Torr. Struck: Structured output tracking with kernels. In ICCV, 2011.
-
(2011)
ICCV
-
-
Hare, S.1
Saffari, A.2
Torr, P.H.S.3
-
15
-
-
84887381620
-
Visual tracking via locality sensitive histograms
-
S. He, Q. Yang, R. W. H. Lau, J. Wang, and M. Yang. Visual tracking via locality sensitive histograms. In CVPR, 2013.
-
(2013)
CVPR
-
-
He, S.1
Yang, Q.2
Lau, R.W.H.3
Wang, J.4
Yang, M.5
-
16
-
-
84963643172
-
Exploiting the circulant structure of tracking-by-detection with kernels
-
J. F. Henriques, R. Caseiro, P. Martins, and J. Batista. Exploiting the circulant structure of tracking-by-detection with kernels. In ECCV, 2012.
-
(2012)
ECCV
-
-
Henriques, J.F.1
Caseiro, R.2
Martins, P.3
Batista, J.4
-
17
-
-
84922907906
-
Highspeed tracking with kernelized correlation filters
-
J. F. Henriques, R. Caseiro, P. Martins, and J. Batista. Highspeed tracking with kernelized correlation filters. TPAMI, 37(3):583-596, 2015.
-
(2015)
TPAMI
, vol.37
, Issue.3
, pp. 583-596
-
-
Henriques, J.F.1
Caseiro, R.2
Martins, P.3
Batista, J.4
-
18
-
-
84969506912
-
Online tracking by learning discriminative saliency map with convolutional neural network
-
S. Hong, T. You, S. Kwak, and B. Han. Online tracking by learning discriminative saliency map with convolutional neural network. In ICML, 2015.
-
(2015)
ICML
-
-
Hong, S.1
You, T.2
Kwak, S.3
Han, B.4
-
19
-
-
84861312439
-
Tracking-learningdetection
-
Z. Kalal, K. Mikolajczyk, and J. Matas. Tracking-learningdetection. TPAMI, 34(7):1409-1422, 2012.
-
(2012)
TPAMI
, vol.34
, Issue.7
, pp. 1409-1422
-
-
Kalal, Z.1
Mikolajczyk, K.2
Matas, J.3
-
20
-
-
84876231242
-
Imagenet classification with deep convolutional neural networks
-
A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
-
(2012)
NIPS
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.E.3
-
21
-
-
85088746113
-
Deeptrack: Learning discriminative feature representations by convolutional neural networks for visual tracking
-
H. Li, Y. Li, and F. Porikli. Deeptrack: Learning discriminative feature representations by convolutional neural networks for visual tracking. In BMVC, 2014.
-
(2014)
BMVC
-
-
Li, H.1
Li, Y.2
Porikli, F.3
-
22
-
-
84885606175
-
A survey of appearance models in visual object tracking
-
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 TIST, 4(4):58, 2013.
-
(2013)
ACM TIST
, vol.4
, Issue.4
, pp. 58
-
-
Li, X.1
Hu, W.2
Shen, C.3
Zhang, Z.4
Dick, A.R.5
Van Den Hengel, A.6
-
23
-
-
0019647180
-
An iterative image registration technique with an application to stereo vision
-
B. D. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. In IJCAI, 1981.
-
(1981)
IJCAI
-
-
Lucas, B.D.1
Kanade, T.2
-
24
-
-
39749173057
-
Incremental learning for robust visual tracking
-
D. A. Ross, J. Lim, R. Lin, and M. Yang. Incremental learning for robust visual tracking. IJCV, 77(1-3):125-141, 2008.
-
(2008)
IJCV
, vol.77
, Issue.1-3
, pp. 125-141
-
-
Ross, D.A.1
Lim, J.2
Lin, R.3
Yang, M.4
-
25
-
-
85083953063
-
Very deep convolutional networks for large-scale image recognition
-
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. ICLR, 2015.
-
(2015)
ICLR
-
-
Simonyan, K.1
Zisserman, A.2
-
26
-
-
84903121415
-
Visual tracking: An experimental survey
-
A. W. M. Smeulders, D. M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, and M. Shah. Visual tracking: An experimental survey. TPAMI, 36(7):1442-1468, 2014.
-
(2014)
TPAMI
, vol.36
, Issue.7
, pp. 1442-1468
-
-
Smeulders, A.W.M.1
Chu, D.M.2
Cucchiara, R.3
Calderara, S.4
Dehghan, A.5
Shah, M.6
-
27
-
-
54749092170
-
80 million tiny images: A large data set for nonparametric object, scene recognition
-
A. Torralba, R. Fergus, and W. T. Freeman. 80 million tiny images: A large data set for nonparametric object and scene recognition. TPAMI, 30(11):1958-1970, 2008.
-
(2008)
TPAMI
, vol.30
, Issue.11
, pp. 1958-1970
-
-
Torralba, A.1
Fergus, R.2
Freeman, W.T.3
-
29
-
-
84924350847
-
Video tracking using learned hierarchical features
-
L. Wang, T. Liu, G. Wang, K. L. Chan, and Q. Yang. Video tracking using learned hierarchical features. TIP, 24(4):1424-1435, 2015.
-
(2015)
TIP
, vol.24
, Issue.4
, pp. 1424-1435
-
-
Wang, L.1
Liu, T.2
Wang, G.3
Chan, K.L.4
Yang, Q.5
-
30
-
-
84898957022
-
Learning a deep compact image representation for visual tracking
-
N. Wang and D. Yeung. Learning a deep compact image representation for visual tracking. In NIPS, 2013.
-
(2013)
NIPS
-
-
Wang, N.1
Yeung, D.2
-
31
-
-
84887348427
-
Online object tracking: A benchmark
-
Y. Wu, J. Lim, and M.-H. Yang. Online object tracking: A benchmark. In CVPR, 2013.
-
(2013)
CVPR
-
-
Wu, Y.1
Lim, J.2
Yang, M.-H.3
-
34
-
-
84950124752
-
MEEM: Robust tracking via multiple experts using entropy minimization
-
J. Zhang, S. Ma, and S. Sclaroff. MEEM: robust tracking via multiple experts using entropy minimization. In ECCV, 2014.
-
(2014)
ECCV
-
-
Zhang, J.1
Ma, S.2
Sclaroff, S.3
-
35
-
-
84956708817
-
Fast visual tracking via dense spatio-temporal context learning
-
K. Zhang, L. Zhang, Q. Liu, D. Zhang, and M.-H. Yang. Fast visual tracking via dense spatio-temporal context learning. In ECCV, 2014.
-
(2014)
ECCV
-
-
Zhang, K.1
Zhang, L.2
Liu, Q.3
Zhang, D.4
Yang, M.-H.5
-
37
-
-
73849098311
-
Differential earth mover's distance with its applications to visual tracking
-
Q. Zhao, Z. Yang, and H. Tao. Differential earth mover's distance with its applications to visual tracking. TPAMI, 32(2):274-287, 2010.
-
(2010)
TPAMI
, vol.32
, Issue.2
, pp. 274-287
-
-
Zhao, Q.1
Yang, Z.2
Tao, H.3
-
38
-
-
84899874514
-
Robust object tracking via sparse collaborative appearance model
-
W. Zhong, H. Lu, and M.-H. Yang. Robust object tracking via sparse collaborative appearance model. TIP, 23(5):2356-2368, 2014.
-
(2014)
TIP
, vol.23
, Issue.5
, pp. 2356-2368
-
-
Zhong, W.1
Lu, H.2
Yang, M.-H.3
-
39
-
-
84877777295
-
Deep learning of invariant features via simulated fixations in video
-
W. Y. Zou, A. Y. Ng, S. Zhu, and K. Yu. Deep learning of invariant features via simulated fixations in video. In NIPS, 2012.
-
(2012)
NIPS
-
-
Zou, W.Y.1
Ng, A.Y.2
Zhu, S.3
Yu, K.4
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