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Online learning has turned out to be effective for improving tracking performance.
Nocedal, J., Wright, S.J.: Numerical Optimization. Springer (1999)
1999
Earlier work this paper cites.
Xing, J., Ai, H., Lao, S.: Multiple human tracking based on multi-view upper-body detection and discriminative learning. In: ICPR (2010)
2010
Earlier work this paper cites.
Liu, L., Xing, J., Ai, H., Ruan, X.: Hand posture recognition using finger geometric feature. In: ICPR (2012)
2012
Earlier work this paper cites.
Danelljan, M., Häger, G., Shahbaz Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: Proceedings of the British Machine Vision Conference. BMVA Press (2014)
2014
Earlier work this paper cites.
Danelljan, M., Shahbaz Khan, F., Felsberg, M., van de Weijer, J.: Adaptive color attributes for real-time visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2014)
2014
Earlier work this paper cites.
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 37
2014
Earlier work this paper cites.
Zhang, J., Ma, S., Sclaroff, S.: MEEM: robust tracking via multiple experts using entropy minimization. In: Proc. of the European Conference on Computer Vision (ECCV) (2014)
2014
Earlier work this paper cites.
Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (December 2015)
2015
Earlier work this paper cites.
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37
2015
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) ICLR (2015)
2015
Earlier work this paper cites.
Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (December 2015)
2015
Earlier work this paper cites.
Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: 2015 IEEE International Conference on Computer Vision (ICCV). pp. 3074–3082 (2015). https://doi.org/10.1109/ICCV.2015.352
2015
Earlier work this paper cites.
Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada (2015)
2015
Earlier work this paper cites.
Wu, Y., Lim, J., Yang, M.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37
2015
Earlier work this paper cites.
Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.H.S.: Staple: Complementary learners for real-time tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2016)
2016
Earlier work this paper cites.
Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: Hua, G., Jégou, H. (eds.) ECCV Workshops (2016)
2016
Earlier work this paper cites.
Danelljan, M., Robinson, A., Khan, F.S., Felsberg, M.: Beyond correlation filters: Learning continuous convolution operators for visual tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Earlier work this paper cites.
Mueller, M., Smith, N., Ghanem, B.: A benchmark and simulator for UAV tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV (2016)
2016
Earlier work this paper cites.
Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: CVPR (2016)
2016
Earlier work this paper cites.
Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2016)
2016
Earlier work this paper cites.
Qi, Y., Zhang, S., Qin, L., Yao, H., Huang, Q., Lim, J., Yang, M.H.: Hedged deep tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2016)
2016
Earlier work this paper cites.
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable convolutional networks. In: ICCV (2017)
2017
Earlier work this paper cites.
Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: ECO: efficient convolution operators for tracking. In: CVPR (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Guo, Q., Feng, W., Zhou, C., Huang, R., Wan, L., Wang, S.: Learning dynamic siamese network for visual object tracking. In: ICCV (2017)
2017
Cited alongside, same era.
Kiani Galoogahi, H., Fagg, A., Lucey, S.: Learning background-aware correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (Oct 2017)
2017
Cited alongside, same era.
Kiani Galoogahi, H., Fagg, A., Lucey, S.: Learning background-aware correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (Oct 2017)
Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: Centernet: Keypoint triplets for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (October 2019)
2019
Later among the works it cites.
Fan, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Bai, H., Xu, Y., Liao, C., Ling, H.: Lasot: A high-quality benchmark for large-scale single object tracking. In: CVPR (2019)
2019
Later among the works it cites.
Fan, H., Ling, H.: Siamese cascaded region proposal networks for real-time visual tracking. In: CVPR (2019)
2019
Later among the works it cites.
Fan, H., Ling, H.: Siamese cascaded region proposal networks for real-time visual tracking. In: CVPR (2019)
2019
Later among the works it cites.
Gao, P., Yuan, R., Wang, F., Xiao, L., Fujita, H., Zhang, Y.: Siamese attentional keypoint network for high performance visual tracking. Knowledge-Based Systems 193
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2017
Cited alongside, same era.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60
2017
Cited alongside, same era.
Lukezic, A., Vojir, T., Zajc, L.C., Matas, J., Kristan, M.: Discriminative correlation filter with channel and spatial reliability. In: CVPR (2017)
2017
Cited alongside, same era.
Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.S.: End-to-end representation learning for correlation filter based tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)
2017
Cited alongside, same era.
Bhat, G., Johnander, J., Danelljan, M., Khan, F.S., Felsberg, M.: Unveiling the power of deep tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV (2018)
2018
Cited alongside, same era.
Huang, L., Zhao, X., Huang, K.: Got-10k: A large high-diversity benchmark for generic object tracking in the wild. CoRR (2018)
2018
Cited alongside, same era.
Jiang, B., Luo, R., Mao, J., Xiao, T., Jiang, Y.: Acquisition of localization confidence for accurate object detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV (2018)
2018
Cited alongside, same era.
Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R.P., Zajc, L.C., et al: The sixth visual object tracking VOT2018 challenge results. In: ECCV Workshops (2018)
2018
Cited alongside, same era.
2019
Later among the works it cites.
Jung, I., You, K., Noh, H., Cho, M., Han, B.: Real-time object tracking and one-shot channel pruning via meta-learning: Efficient model adaptation. CoRR (2019)
2019
Later among the works it cites.
Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., Yan, J.: Siamrpn++: Evolution of siamese visual tracking with very deep networks. In: CVPR (2019)
2019
Later among the works it cites.
Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. CoRR (2019)
2019
Later among the works it cites.
Wang, G., Luo, C., Xiong, Z., Zeng, W.: Spm-tracker: Series-parallel matching for real-time visual object tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
2019
Later among the works it cites.
Xu, T., Feng, Z.H., Wu, X.J., Kittler, J.: Joint group feature selection and discriminative filter learning for robust visual object tracking. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (October 2019)
2019
Later among the works it cites.
Xu, Y., Wang, Z., Li, Z., Ye, Y., Yu, G.: Siamfc++: Towards robust and accurate visual tracking with target estimation guidelines. CoRR (2019)
2019
Later among the works it cites.
Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable convnets V2: more deformable, better results. In: CVPR (2019)
2019
Later among the works it cites.
Chen, Z., Zhong, B., Li, G., Zhang, S., Ji, R.: Siamese box adaptive network for visual tracking. In: CVPR (June 2020)
2020
Closest in time.
Danelljan, M., Van Gool, L., Timofte, R.: Probabilistic regression for visual tracking. In: CVPR (2020)
2020
Closest in time.
Du, F., Liu, P., Zhao, W., Tang, X.: Correlation-guided attention for corner detection based visual tracking. In: CVPR (June 2020)
2020
Closest in time.
Guo, D., Wang, J., Cui, Y., Wang, Z., Chen, S.: Siamcar: Siamese fully convolutional classification and regression for visual tracking. In: CVPR (June 2020)
2020
Closest in time.
Voigtlaender, P., Luiten, J., Torr, P.H., Leibe, B.: Siam r-cnn: Visual tracking by re-detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)
2020
Closest in time.
Zhang, Z., Peng, H., Fu, J., Li, B., Hu, W.: Ocean: Object-aware anchor-free tracking. In: ECCV (2020)
2020
Closest in time.
Chen, X., Yan, B., Zhu, J., Wang, D., Yang, X., Lu, H.: Transformer tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 8126–8135 (June 2021)
2021
Closest in time.
Cui, Y., Jiang, C., Wang, L., Wu, G.: Target transformed regression for accurate tracking (2021)
2021
Closest in time.
Fu, Z., Liu, Q., Fu, Z., Wang, Y.: Stmtrack: Template-free visual tracking with space-time memory networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 13774–13783 (June 2021)
2021
Closest in time.
Wang, N., Zhou, W., Wang, J., Li, H.: Transformer meets tracker: Exploiting temporal context for robust visual tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1571–1580 (June 2021)
2021
Closest in time.