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In the field of generic object tracking numerous attempts have been made to exploit deep features.
Matrix analysis
Horn, R.A., Johnson, C.R.: · 1990
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Histograms of oriented gradients for human detection
Dalal, N., Triggs, B.: · 2005
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Learning color names for real-world applications
van de Weijer, J., Schmid, C., Verbeek, J.J., Larlus, D.: · 2009
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Visual object tracking using adaptive correlation filters
Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: · 2010
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Struck: Structured output tracking with kernels
Hare, S., Saffari, A., Torr, P.: · 2011
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Visual tracking via locality sensitive histograms
He, S., Yang, Q., Lau, R., Wang, J., Yang, M.H.: · 2013
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MEEM: robust tracking via multiple experts using entropy minimization
Zhang, J., Ma, S., Sclaroff, S.: · 2014
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Deeptrack: Learning discriminative feature representations by convolutional neural networks for visual tracking
Li, H., Li, Y., Porikli, F.: · 2014
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A scale adaptive kernel correlation filter tracker with feature integration
Li, Y., Zhu, J.: · 2014
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Matconvnet – convolutional neural networks for matlab
Vedaldi, A., Lenc, K.: · 2014
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Return of the devil in the details: Delving deep into convolutional nets
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: · 2014
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A scale adaptive kernel correlation filter tracker with feature integration
Li, Y., Zhu, J.: · 2014
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Object tracking benchmark
Wu, Y., Lim, J., Yang, M.H.: · 2015
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Hierarchical convolutional features for visual tracking
Ma, C., Huang, J.B., Yang, X., Yang, M.H.: · 2015
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Visual tracking with fully convolutional networks
Wang, L., Ouyang, W., Wang, X., Lu, H.: · 2015
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Learning spatially regularized correlation filters for visual tracking
Danelljan, M., Häger, G., Shahbaz Khan, F., Felsberg, M.: · 2015
Cited alongside, same era.
Multi-store tracker (muster): A cognitive psychology inspired approach to object tracking
Hong, Z., Chen, Z., Wang, C., Mei, X., Prokhorov, D., Tao, D.: · 2015
Cited alongside, same era.
Encoding color information for visual tracking: Algorithms and benchmark
Liang, P., Blasch, E., Ling, H.: · 2015
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
Cited alongside, same era.
Convolutional features for correlation filter based visual tracking
Danelljan, M., Häger, G., Shahbaz Khan, F., Felsberg, M.: · 2015
Cited alongside, same era.
Staple: Complementary learners for real-time tracking
Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.H.S.: · 2016
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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The visual object tracking vot2016 challenge results
Kristan, M., Leonardis, A., Matas, J., Felsberg, Pflugfelder, R., M., Čehovin, L., Vojír, T.and Häger, G., et al · 2016
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Discriminative scale space tracking
Danelljan, M., Hager, G., Khan, F.S., Felsberg, M.: · 2016
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ECO: efficient convolution operators for tracking
Danelljan, M., Bhat, G., Shahbaz Khan, F., Felsberg, M.: · 2017
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The visual object tracking vot2017 challenge results
Kristan, M., Leonardis, A., Matas, J., Felsberg, Pflugfelder, R., M., Čehovin, L., Vojír, T.and Häger, G., et al · 2017
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Need for speed: A benchmark for higher frame rate object tracking
Galoogahi, H.K., Fagg, A., Huang, C., Ramanan, D., Lucey, S.: · 2017
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Cited alongside, same era.
Beyond correlation filters: Learning continuous convolution operators for visual tracking
Danelljan, M., Robinson, A., Khan, F., Felsberg, M.: · 2016
Cited alongside, same era.
Siamese instance search for tracking
Tao, R., Gavves, E., Smeulders, A.W.M.: · 2016
Cited alongside, same era.
Learning multi-domain convolutional neural networks for visual tracking
Nam, H., Han, B.: · 2016
Cited alongside, same era.
Modeling and propagating cnns in a tree structure for visual tracking
Nam, H., Baek, M., Han, B.: · 2016
Cited alongside, same era.
Fully-convolutional siamese networks for object tracking
Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.: · 2016
Cited alongside, same era.
Hedged deep tracking
Qi, Y., Zhang, S., Qin, L., Yao, H., Huang, Q., Lim, J., Yang, M.H.: · 2016
Cited alongside, same era.
Later among the works it cites.
End-to-end representation learning for correlation filter based tracking
Valmadre, J., Bertinetto, L., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: · 2017
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CREST: Convolutional residual learning for visual tracking
Song, Y., Ma, C., Gong, L., Zhang, J., Lau, R., Yang, M.H.: · 2017
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Multi-task correlation particle filter for robust object tracking
Zhang, T., Xu, C., Yang, M.H.: · 2017
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Learning background-aware correlation filters for visual tracking
Kiani Galoogahi, H., Fagg, A., Lucey, S.: · 2017
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VITAL: Visual tracking via adversarial learning
Song, Y., Ma, C., Wu, X., Gong, L., Bao, L., Zuo, W., Shen, C., Lau, R., Yang, M.H.: · 2018
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A twofold siamese network for real-time object tracking
He, A., Luo, C., Tian, X., Zeng, W.: · 2018
Closest in time.
End-to-end flow correlation tracking with spatial-temporal attention
Zhu, Z., Wu, W., Zou, W., Yan, J.: · 2018
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Learning spatial-aware regressions for visual tracking
Sun, C., Lu, H., Yang, M.H.: · 2018
Closest in time.