Open Access
Volume 37, Number 6, December 2019
Page(s) 1310 - 1319
Published online 11 February 2020
  1. Laurense V A, Goh J Y, Gerdes J C. Path-Tracking for Autonomous Vehicles at the Limit of Friction[C]//Proceedings of the American Control Conference(ACC), 2017: 5586–5591 [Google Scholar]
  2. Sivanantham S, Paul N N, Iyer R S. Object Tracking Algorithm Implementation for Security Applications[J]. Far East Journal of Electronics and Communications, 2016, 16(1): 1–13 [Article] [CrossRef] [Google Scholar]
  3. Onate J M B, Chipantasi D J M, Erazo N R V. Tracking Objects Using Artificial Neural Networks and Wireless Connection for Robotics[J]. Journal of Telecommunication, Electronic and Computer Engineering, 2017, 9(1/2/3): 161–164 [Article] [Google Scholar]
  4. Perez P, Hue C, Vermaak J, et al. Color-Based Probabilistic Tracking[C]//Proceedings of European Conference on Computer Vision, 2002: 661–675 [Google Scholar]
  5. Wang Z, Yang X, Xu Y, et al. CamShift Guided Particle Filter for Visual Tracking[J]. Pattern Recognition Letters, 2009, 30(4): 407–413 [Article] [CrossRef] [Google Scholar]
  6. Li Guanbin, Wu Hefeng. Weighted Fragments-Based Meanshift Tracking Using Color-Texture Histogram[J]. Journal of Computer-Aided Design and Computer Graphics, 2011, 23(12): 2059–2066 [Article] (in Chinese) [Google Scholar]
  7. Hare S, Saffari A, Torr P H S. Struck: Structured Output Tracking with Kernels[C]//Proceedings of the IEEE International Conference on Computer Vision, 2011: 263–270 [Google Scholar]
  8. Henriques, João F, Caseiro R, Martins P, et al. High-Speed Tracking with Kernelized Correlation Filters[J]. IEEE Trans on Pattern Analysis & Machine Intelligence, 2014, 37(3): 583–596 [Google Scholar]
  9. Babenko B, Yang M H, Belongies S. Robust Object Tracking with Online Multiple Instance Learning[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619–1632 [Article] [CrossRef] [Google Scholar]
  10. Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks[C]//Proceedings of International Conference on Neural Information Processing Systems, 2012: 1097–1105 [Google Scholar]
  11. Danelljan M, Gustav Häger, Khan F S, et al. Convolutional Features for Correlation Filter Based Visual Tracking[C]//Proceedings of IEEE International Conference on Computer Vision Workshop, 2016: 621–629 [Google Scholar]
  12. Nam H, Han B. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognitionm, 2016: 4293–4302 [Google Scholar]
  13. Sutskever I, Vinyals O, Le Q V. Sequence to Sequence Learning with Neural Networks[C]//Proceedings of Advances in Neural Information Processing Systems, 2014: 3104–3112 [Google Scholar]
  14. Graves A, Mohamed A, Hinton G. Speech Recognition with Deep Recurrent Neural Networks[C]//Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 2013: 6645–6649 [Google Scholar]
  15. Cui Z, Xiao S, Feng J, et al. Recurrently Target-Attending Tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2016: 1449–1458 [Google Scholar]
  16. Greff K, Srivastava R K, Koutník J, et al. LSTM:A Search Space Odyssey[J]. IEEE Trans on Neural Networks and Learning Systems, 2017, 28(10): 2222–2232 [Article] [Google Scholar]
  17. Russakovsky O, Deng J, Su H, et al. Image Net Large Scale Visual Recognition Challenge[J]. International Journal of Computer Vision, 2014, 115(3): 211–252 [Article] [Google Scholar]
  18. Vedaldi A, Lenc K. Matconvnet: Convolutional Neural Networks for Matlab[C]//Proceedings of the 23rd ACM International Conference on Multimedia, 2015: 689–692 [Google Scholar]
  19. Mvller M, Bibi A, Giancola S, et al. Tracking Net: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild[C]//Proceedings of European Conference on Computer Vision, 2018: 310–327 [Google Scholar]
  20. Wu Y, Lim J, Yang M H. Object Tracking Benchmark[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834–1848 [Article] [CrossRef] [Google Scholar]
  21. Danelljan M, Hager G, Shahbaz KHAN F, et al. Learning Spatially Regularized Correlation Filters for Visual Tracking[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 4310–4318 [Google Scholar]
  22. Valmadre J, Bertinetto L, Henriques J, et al. End-to-End Representation Learning for Correlation Filter Based Tracking[C]//Proceedings of Computer Vision and Pattern Recognition, 2017: 5000–5008 [Google Scholar]

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