基于卷积神经网络的响应自适应跟踪
[1] KWON J, LEE K M. Visual tracking decomposition [C]//Proceedings of 2010 IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA: IEEE, 2010: 1269-1276.
[2] SHARMA S, KHACHANE A, MOTWANI D. Real time multi-object tracking using TLD framework [C]//Proceedings of 2016 International Conference on Inventive Computation Technologies. Coimbatore, India: IEEE, 2016: 1-6.
[3] BABENKO B, YANG M H, BELONGIE S. Robust object tracking with online multiple instance learning [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619-1632.
[4] HERE S, SAFFARI A, TORR P H S. Struck: structured output tracking with kernels [C]//Proceedings of 2011 IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011: 263-270.
[5] ZHAO M, QIAN H M, RONG Y J, et al. Robust object tracking via sparse representation based on compressive collaborative Haar-like feature space [C]//Proceedings of 2016 International Conference on Audio, Language and Image Processing. Shanghai, China: IEEE, 2016: 274-278.
[6] LIU Y X, ZHANG Y Z, HU M Y, et al. Fast tracking via spatio-temporal context learning based on multi-color attributes and pca [C]//Proceedings of 2017 IEEE International Conference on Information and Automation. Macau, China: IEEE, 2017: 398-403.
[7] 张雷,王延杰,孙宏海,等.采用核相关滤波器的自适应尺度目标跟踪[J].光学 精密工程,2016,24(2): 448-459.
[8] HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-speed tracking with kernelized correlation filters [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583-596.
[9] 王暐,王春平,李军,等.特征融合和模型自适应更新相结合的相关滤波目标跟踪[J].光学 精密工程,2016,24(8): 2059-2066.
[10] WU Y, LIM J, YANG M H. Online object tracking: a benchmark [C]//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR, USA: IEEE, 2013: 2411-2418.
[11] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition \[OL\]. eprint arXiv: 1409.1556, 2015.
[12] DENG J, DONG W, SOCHER R, et al. Imagenet: a large-scale hierarchical image database[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA: IEEE, 2009: 248-255.
[13] Adel B, Matthias M, Bernard G, et al. Target response adaptation for correlation filter tracking[C]. European Conference on Computer Vision, 2016: 1-6.
[14] GAO J, LING H B, HU W M, et al. Transfer learning based visual tracking with Gaussian processes regression [C]//FLEET D, PAJDLA T, SCHIELE B, et al. Computer Vision – ECCV 2014. Cham: Springer, 2014, 8691: 188-203.
[15] ZHANG K H, ZHANG L, YANG M H. Real-time compressive tracking [C]//FITZGIBBON A, LAZEBNIK S, PERONA P, et al. Computer Vision – ECCV 2012. Berlin, Heidelberg: Springer, 2012, 7574: 864-877.
[16] WANG N Y, YEUNG D Y. Learning a deep compact image representation for visual tracking [C]//Proceedings of the 26th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada: ACM, 2013: 809-817.
[17] 刘扬,张云峰,董月芳.复杂背景下抗遮挡的运动目标跟踪算法[J].液晶与显示,2010,25(6): 890-895.
[18] 杨德东,毛宁,杨福才,等.利用最佳伙伴相似性的改进空间正则化判别相关滤波目标跟踪[J].光学 精密工程,2018,26(2): 492-502.
李勇, 杨德东, 毛宁, 李雪晴. 基于卷积神经网络的响应自适应跟踪[J]. 液晶与显示, 2018, 33(7): 596. LI Yong, YANG De-dong, MAO Ning, LI Xue-qing. Response adaptive tracking based on convolution neural network[J]. Chinese Journal of Liquid Crystals and Displays, 2018, 33(7): 596.