视觉跟踪算法综述
A Review of Visual Tracking
DOI: 10.12677/CSA.2018.81006, PDF,   
作者: 王楠洋*, 谢志宏, 杨 皓:陆军装甲兵学院控制系光电室,北京
关键词: 计算机视觉视觉跟踪算法Computer Vision Visual Tracking Algorithm
摘要: 视觉跟踪是计算机视觉领域多年来的研究热点问题之一,随着技术的不断发展,视觉跟踪应用范围逐渐扩大。本文首先阐述了跟踪过程中的难点和视觉跟踪的应用,以及跟踪方法的两大分类。以时间顺序对跟踪算法的发展历史及现状进行介绍,并比较分析其优缺点,最后探讨了视觉跟踪算法未来的发展趋势。
Abstract: Visual tracking is one of the hot topics in the field of computer vision. Over the years, the application of visual tracking has been gradually expanded with the continuous development of technology. First, the difficulties of tracking process and the application of visual tracking are introduced in this paper. Then, tracking methods are classified into two major classifications. The history of development and current research status of the tracking algorithm are introduced in time sequence, and the advantages and disadvantages are analyzed. Finally, the future trend of visual tracking is presented.
文章引用:王楠洋, 谢志宏, 杨皓. 视觉跟踪算法综述[J]. 计算机科学与应用, 2018, 8(1): 35-42. https://doi.org/10.12677/CSA.2018.81006

参考文献

[1] 朱文青, 刘艳, 卞乐, 张子龙. 基于生成式模型的目标跟踪方法综述[J]. 微处理机, 2017, 38(1): 41-47.
[2] 尹宏鹏, 陈波, 柴毅, 等. 基于视觉的目标检测与跟踪综述[J]. 自动化学报, 2016, 42(10): 1466-1489.
[3] Yang, H., Shao, L., Zheng, F., et al. (2011) Recent Advances and Trends in Visual Tracking: A Review. Neurocomputing, 74, 3823-3831. [Google Scholar] [CrossRef
[4] 魏全禄, 老松杨, 白亮. 基于相关滤波器的视觉目标跟踪综述[J]. 计算机科学, 2016, 43(11): 1-5.
[5] Babenko, B. (2009) Visual Tracking with Online Multiple Instance Learning. CVPR.
[6] Bolme, D.S., Beveridge, J.R., Draper, B.A. and Lui, Y.M. (2010) Visual Object Tracking Using Adaptive Correlation Filters. 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 13-18 2010, San Francisco. [Google Scholar] [CrossRef
[7] Kalal, Z., Mikolajczyk, K. and Matas, J. (2012) Track-ing-Learning-Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 1409-1422. [Google Scholar] [CrossRef
[8] Hare, S., Golodetz, S., Saffari, A., et al. (2016) Struck: Structured Output Tracking with Kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 2096-2109. [Google Scholar] [CrossRef
[9] Henriques, F., Caseiro, R., Martins, P., et al. (2012) Exploiting the Circulant Structure of Tracking-by-Detection with Kernels. European Conference on Computer Vision, 702-715. [Google Scholar] [CrossRef
[10] Wu, Y., Lim, J. and Yang, M.H. (2013) Online Object Tracking: A Benchmark. 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 23-28 2013, Portland. [Google Scholar] [CrossRef
[11] Henriques, J.F., Rui, C., Martins, P., et al. (2015) High-Speed Tracking with Kernelized Correlation Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 583-596.
[12] Li, Y. and Zhu, J. (2014) A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration. European Conference on Computer Vision, Zurich, 6-12 September 2014, 254-265.
[13] Danelljan, M., Häger, G., Khan, F., et al. (2014) Accurate Scale Estimation for Robust Visual Tracking. Proceedings British Machine Vision Conference, Nottingham, 1-5 September 2014, 1-11. [Google Scholar] [CrossRef
[14] Zhang, K., Zhang, L., Yang, M.H., et al. (2013) Fast Tracking via Spa-tio-Temporal Context Learning.
[15] Wang, N., Li, S., Gupta, A., et al. (2015) Transferring Rich Feature Hierarchies for Robust Visual Tracking.
[16] Wang, N. and Yeung, D.Y. (2013) Learning a Deep Compact Image Representation for Visual Tracking. International Conference on Neural Information Processing Systems, Lake Tahoe, 5-10 December 2013, 809-817.
[17] Ma, C., Huang, J.-B., Yang, X., et al. (2015) Hierarchical Convolutional Features for Visual Tracking.
[18] Danelljan, M., Hager, G., Khan, F.S., et al. (2015) Convolutional Features for Correlation Filter Based Visual Tracking. IEEE International Conference on Computer Vision Workshop, Santiago, 7-13 December 2015, 621-629. [Google Scholar] [CrossRef
[19] Danelljan, M., Hager, G., Khan, F.S., et al. (2015) Learning Spatially Regularized Correlation Filters for Visual Tracking. ICCV, Chile, 7-13 December 2015, 4310-4318. [Google Scholar] [CrossRef
[20] Han, B. (2016) Learning Multi-Domain Convolutional Neural Networks for Visual Tracking. CVPR, Las Vegas, 26 June-1 July 2016, 4293-4302.
[21] Danelljan, M., Robinson, A., Khan, F., et al. (2016) Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking. ECCV, Amsterdam, 11-14 October 2016, 1-16.
[22] Nam, H., Baek, M. and Han, B. (2016) Modeling and Propagating CNNs in a Tree Structure for Visual Tracking.
[23] Bertinetto, L., Valmadre, J. Golodetz, S., et al. (2015) Staple: Complementary Learners for Real-Time Tracking. International Conference on Computer Vision and Pattern, Quebec City, 27-30 September 2015, Vol. 38, 1401-1409.
[24] Bertinetto, L., Valmadre, J., Henriques, J.F., et al. (2016) Fully-Convolutional Siamese Networks for Object Tracking. ECCV, Amsterdam, 11-14 October 2016, 1-16. [Google Scholar] [CrossRef
[25] Danelljan, M., Bhat, G., Khan, F.S., et al. (2016) ECO: Efficient Convolution Operators for Tracking. Computer Vision and Pattern Recognition, Las Vegas, 26 June-1 July 2016, 6638-6646.