[1]
|
张铁, 马琼雄.人机交互中的人体目标跟踪算法.上海交通大学学报, 2015, 49(8):1213-1219 http://d.old.wanfangdata.com.cn/Periodical/shjtdxxb201508021Zhang Tie, Ma Qiong-Xiong. Human object tracking algorithm for human-robot interaction. Journal of Shanghai Jiao Tong University, 2015, 49(8):1213-1219 http://d.old.wanfangdata.com.cn/Periodical/shjtdxxb201508021
|
[2]
|
Pantrigo J J, Hernández J, Sánchez A. Multiple and variable target visual tracking for video-surveillance applications. Pattern Recognition Letters, 2010, 31(12):1577-1590 doi: 10.1016/j.patrec.2010.04.017
|
[3]
|
权义萍, 杨道业.基于视频检测的卡尔曼滤波车辆跟踪算法及行为分析.北京工业大学学报, 2014, 40(7):1110-1113 http://d.old.wanfangdata.com.cn/Periodical/bjgydxxb201407026Quan Yi-Ping, Yang Dao-Ye. Kalman filter vehicle tracking algorithm and behaviour analysis based on video detection. Journal of Beijing University of Technology, 2014, 40(7):1110-1113 http://d.old.wanfangdata.com.cn/Periodical/bjgydxxb201407026
|
[4]
|
Yang G, Zhao J S, Zheng C H, Fan Y. An approach based on mean shift and background difference for moving object tracking. In: Proceedings of the 6th International Conference on Wireless Communications Networking and Mobile Computing. Chengdu, China: IEEE, 2010. 1-4 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=5600705
|
[5]
|
Horn B K P, Schunck B G. Determining optical flow. Artificial Intelligence, 1981, 17(1-3):185-203 doi: 10.1016/0004-3702(81)90024-2
|
[6]
|
Nam H, Han B. Learning multi-domain convolutional neural networks for visual tracking. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016. 4293-4302 http://ieeexplore.ieee.org/document/7780834/
|
[7]
|
Bertinetto L, Valmadre J, Henriques J F, Vedaldi A, Torr P H S. Fully-convolutional siamese networks for object tracking. In: Proceedings of the 2016 European Conference on Computer Vision. Amsterdam, Netherlands: Springer, 2016. 850-865 doi: 10.1007/978-3-319-48881-3_56
|
[8]
|
Wang L J, Ouyang W L, Wang X G, Lu H C. STCT: sequentially training convolutional networks for visual tracking. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016. 1373-1381
|
[9]
|
Danelljan M, Bhat G, Khan F S, Felsberg M. ECO: efficient convolution operators for tracking. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017. 6931-6939
|
[10]
|
Hong Z B, Chen Z, Wang C H, Mei X, Prokhorov D, Tao D C. MUlti-store tracker (MUSTer): a cognitive psychology inspired approach to object tracking. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 2015. 749-758 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7298675
|
[11]
|
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 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017. 5000-5008 http://www.researchgate.net/publication/320971954_End-to-End_Representation_Learning_for_Correlation_Filter_Based_Tracking
|
[12]
|
Zhang J M, Ma S G, Sclaroff S. MEEM: robust tracking via multiple experts using entropy minimization. In: Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014. 188-203 doi: 10.1007/978-3-319-10599-4_13
|
[13]
|
Possegger H, Mauthner T, Bischof H. In defense of color-based model-free tracking. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 2015. 2113-2120
|
[14]
|
Adankon M M, Cheriet M. Support vector machine. Computer Science, 2002, 1(4):1-28 http://d.old.wanfangdata.com.cn/Periodical/wlhxxb201705012
|
[15]
|
Bolme D S, Beveridge J R, Draper B A, Lui Y M. Visual object tracking using adaptive correlation filters. In: Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA: IEEE, 2010. 2544-2550 http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=5539960
|
[16]
|
Henriques J F, Caseiro R, Martins P, Batista J. Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of the 12th European Conference on Computer Vision. Florence, Italy: Springer, 2012. 702-715
|
[17]
|
Henriques J F, Caseiro R, Martins P, Batista J. High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 37(3):583-596 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=c8f7e9e032e4e419c5c79d7a5f1f6494
|
[18]
|
Danelljan M, Khan F S, Felsberg M, van de Weijer J. Adaptive color attributes for real-time visual tracking. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA: IEEE, 2014. 1090-1097 http://ieeexplore.ieee.org/document/6909539/
|
[19]
|
Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr P H S. Staple: complementary learners for real-time tracking. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016. 1401-1409
|
[20]
|
Danelljan M, Häger G, Khan F, Felsberg M. Accurate scale estimation for robust visual tracking. In: Proceedings of the 2014 British Machine Vision Conference. Michel, Canada: BMVA Press, 2014. 1-65
|
[21]
|
Danelljan M, Häger G, Khan F S, Felsberg M. Discriminative scale space tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8):1561-1575 doi: 10.1109/TPAMI.2016.2609928
|
[22]
|
Zhang M D, Xing J L, Gao J, Hu W M. Robust visual tracking using joint scale-spatial correlation filters. In: Proceedings of the 2015 IEEE International Conference on Image Processing. Quebec City, QC, Canada: IEEE, 2015. 1468-1472
|
[23]
|
Zhang M D, Xing J L, Gao J, Shi X C, Wang Q, Hu W M. Joint scale-spatial correlation tracking with adaptive rotation estimation. In: Proceedings of the 2015 IEEE International Conference on Computer Vision Workshop. Santiago, Chile: IEEE, 2015. 595-603 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7406430
|
[24]
|
Ma C, Yang X K, Zhang C Y, Yang M H. Long-term correlation tracking. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 2015. 5388-5396
|
[25]
|
Danelljan M, Häger G, Khan F S, Felsberg M. Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015. 4310-4318
|
[26]
|
Danelljan M, Häger G, Khan F S, Felsberg M. Convolutional features for correlation filter based visual tracking. In: Proceedings of the 2015 IEEE International Conference on Computer Vision Workshop. Santiago, Chile: IEEE, 2015. 621-629
|
[27]
|
Wang Q, Gao J, Xing J L, Zhang M D, Hu W M. DCFNet: discriminant correlation filters network for visual tracking. arXiv: 1704.04057. 2017.
|
[28]
|
Danelljan M, Häger G, Khan F S, Felsberg M. Adaptive decontamination of the training set: a unified formulation for discriminative visual tracking. In: Proceedings of the 2016 IEEE Conference on Computer Vision and pattern Recognition. Las Vegas, NV, USA: IEEE, 2016. 1430-1438
|
[29]
|
Danelljan M, Robinson A, Khan F S, Felsberg M. Beyond correlation filters: learning continuous convolution operators for visual tracking. In: Proceedings of the 14th Computer Vision. Amsterdam, Netherlands: Springer, 2016. 472-488
|
[30]
|
Yi W, Lim J, Yang M H. Online object tracking: a benchmark. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR, USA: IEEE, 2013. 2411-2418
|
[31]
|
Kristan M, Leonardis A, Matas J, Felsberg M, Pflugfelder R, Ćuehovin L, et al. The visual object tracking vot2016 challenge results. In: Proceedings of the 2016 European Conference on Computer Vision. Amsterdam, Netherlands: Springer, 2016. 777-823
|
[32]
|
Nam H, Baek M, Han B. Modeling and propagating CNNs in a tree structure for visual tracking. arXiv: 1608.07242. 2016.
|