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We propose a new long-term tracking performance evaluation methodology and present a new challenging dataset of carefully selected sequences with many target disappearances.
The PASCAL visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: · 2010
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Tracking-learning-detection
Kalal, Z., Mikolajczyk, K., Matas, J.: · 2012
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Online object tracking: A benchmark
Wu, Y., Lim, J., Yang, M.H.: · 2013
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The visual object tracking vot2013 challenge results
Kristan, M., Pflugfelder, R., Leonardis, A., Matas, J., Porikli, F., Čehovin, L., Nebehay, G., Fernandez, G., Vojir, T.e.a.: · 2013
Earlier work this paper cites.
Object tracking by oversampling local features
Pernici, F., Del Bimbo, A.: · 2013
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Matrioska: A multi-level approach to fast tracking by learning
Maresca, M.E., Petrosino, A.: · 2013
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Visual tracking: An experimental survey
Smeulders, A., Chu, D., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: · 2014
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Encoding color information for visual tracking: Algorithms and benchmark
Liang, P., Blasch, E., Ling, H.: · 2015
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MOTChallenge 2015: Towards a benchmark for multi-target tracking
Leal-Taixé, L., Milan, A., Reid, I., Roth, S., Schindler, K.: · 2015
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Object tracking benchmark
Wu, Y., Lim, J., Yang, M.H.: · 2015
Earlier work this paper cites.
High-speed tracking with kernelized correlation filters
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: · 2015
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Learning spatially regularized correlation filters for visual tracking
Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: · 2015
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Convolutional features for correlation filter based visual tracking
Danelljan, M., Häger, G., Khan, F.S., Felsberg, M.: · 2015
Cited alongside, same era.
Clustering of static-adaptive correspondences for deformable object tracking
Nebehay, G., Pflugfelder, R.: · 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.
Long-term correlation tracking
Ma, C., Yang, X., Zhang, C., Yang, M.H.: · 2015
Cited alongside, same era.
A novel performance evaluation methodology for single-target trackers
Kristan, M., Matas, J., Leonardis, A., Vojir, T., Pflugfelder, R., Fernandez, G., Nebehay, G., Porikli, F., Cehovin, L.: · 2016
Cited alongside, same era.
A benchmark and simulator for uav tracking
Mueller, M., Smith, N., Ghanem, B.: · 2016
The visual object tracking vot2017 challenge results
Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., Cehovin Zajc, L., Vojir, T., Hager, G., Lukezic, A., Eldesokey, A., Fernandez, G.: · 2017
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Long-term visual object tracking benchmark
Moudgil, A., Gandhi, V.: · 2017
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Tao, R., Gavves, E., Smeulders, A.W.: · 2017
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Need for speed: A benchmark for higher frame rate object tracking
Kiani Galoogahi, H., Fagg, A., Huang, C., Ramanan, D., Lucey, S.: · 2017
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Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking
Cehovin Zajc, L., Lukezic, A., Leonardis, A., Kristan, M.: · 2017
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Cited alongside, same era.
Visual object tracking performance measures revisited
Čehovin, L., Leonardis, A., Kristan, M.: · 2016
Cited alongside, same era.
Learning multi-domain convolutional neural networks for visual tracking
Nam, H., 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.
Siamese instance search for tracking
Tao, R., Gavves, E., Smeulders, A.W.M.: · 2016
Cited alongside, same era.
The visual object tracking vot2016 challenge results
Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., Čehovin, L., Vojir, T., Häger, G., Lukežič, A., et al. Fernandez, G.: · 2016
Cited alongside, same era.
Discriminative correlation filter with channel and spatial reliability
Lukežič, A., Vojíř, T., Čehovin Zajc, L., Matas, J., Kristan, M.: · 2017
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Parallel tracking and verifying: A framework for real-time and high accuracy visual tracking
Fan, H., Ling, H.: · 2017
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FCLT - A fully-correlational long-term tracker
Lukezic, A., Zajc, L.C., Vojír, T., Matas, J., Kristan, M.: · 2017
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Eco: Efficient convolution operators for tracking
Danelljan, M., Bhat, G., Shahbaz Khan, F., Felsberg, M.: · 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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Crest: Convolutional residual learning for visual tracking
Song, Y., Ma, C., Gong, L., Zhang, J., Lau, R.W.H., Yang, M.H.: · 2017
Later among the works it cites.