[3] Nguyen H T. Smeulders A W M. Fast occluded object tracking by a robust appearance filter[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 1099-1104(2004).
[4] Comaniciu D, Ramesh V, Meer P. Real-time tracking of non-rigid objects using mean shift. [C]∥Proceedings IEEE Conference on Computer Vision and Pattern Recognition, 142-149(2000).
[5] Babenko B, Yang M H, Belongie S. Visual tracking with online Multiple Instance Learning. [C]∥Computer Vision and Pattern Recognition, 983-990(2009).
[6] Kalal Z, Matas J, Mikolajczyk K. P-N learning: Bootstrapping binary classifiers by structural constraints. [C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 49-56(2010).
[7] Hare S, Saffari A. Torr P H S. Struck: Structured output tracking with kernels. [C]∥International Conference on Computer Vision, 263-270(2011).
[9] Bolme D S, Beveridge J R, Draper B A et al. Visual object tracking using adaptive correlation filters. [C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2544-2550(2010).
[11] Xiang Y, Alahi A, Savarese S. Learning to track: online multi-object tracking by decision making. [C]∥IEEE International Conference on Computer Vision (ICCV), 4705-4713(2015).
[12] Danelljan M, Häger G, Shahbaz Khan F et al. Accurate scale estimation for robust visual tracking. [C]∥Proceedings of the British Machine Vision Conference 2014, British Machine Vision Conference(2014).
[13] Li Y, Zhu J K[M]. A scale adaptive kernel correlation filter tracker with feature integration, 254-265(2015).
[14] Bertinetto L, Valmadre J, Golodetz S et al. Staple: complementary learners for real-time tracking. [C]∥IEEE Computer Vision and Pattern Recognition, 1401-1409(2016).
[18] Kalal Z, Mikolajczyk K, Matas J. Forward-backward error: automatic detection of tracking failures. [C]∥20 th International Conference on Pattern Recognition , 23-26(2010).