[1] 董永坤, 王春香, 薛林继, 等. 基于TLD框架的行人检测和跟踪[J]. 华中科技大学学报(自然科学版), 2013, 41(s1): 226-228.

    Dong Y K, Wang C X, Xue L J, et al. Pedestrian detection and tracking based on TLD framework[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2013, 41(s1): 226-228.

[2] Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2012, 34(7): 1409-1422.

[3] 秦飞, 汪荣贵, 梁启香, 等. 基于关键特征点的改进TLD目标跟踪算法研究[J]. 计算机工程与应用, 2016, 52(4): 181-187.

    Qin F, Wang R G, Liang Q X, et al. A Improved TLD target tracking algorithm based on key feature points[J]. Computer Engineering and Applications, 2016, 52(4): 181-187.

[4] 易诗, 林凡强, 周姝颖. 基于改进TLD的自动目标跟踪方法[J]. 重庆邮电大学学报(自然科学版), 2016, 28(6): 892-896.

    Yi S, Lin F Q, Zhou S Y. Automatic tracking method based on improved TLD[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2016, 28(6): 892-896.

[5] 金哲, 刘传才. 加速的TLD算法及其在多目标跟踪中的应用[J]. 计算机系统应用, 2016, 25(6): 196-201.

    Jin Z, Liu C C. Accelerated TLD algorithm and its application in multiple target tracking[J]. Computer System and Applications, 2016, 25(6): 196-201.

[6] 焦蓬斐, 秦品乐, 苗启广, 等. 基于多新息Kalman滤波的TLD改进算法[J]. 数据采集与处理, 2016, 31(3): 592-598.

    Jiao P F, Qin P L, Miao Q G, et al. Improved TLD algorithm based on multi-innovation Kalman filter[J]. Journal of Data Acquisition and Processing, 2016, 31(3): 592-598.

[7] 肖庆国, 叶庆卫, 周宇, 等. 基于Mean-Shift优化的TLD视频长时间跟踪算法[J]. 计算机应用研究, 2015, 32(3): 925-928.

    Xiao Q G, Ye Q W, Zhou Y, et al. Long-time video tracking algorithm of optimized TLD based on Mean-Shife[J]. Computer Application Research, 2015, 32(3): 925-928.

[8] 孙保基, 张葆, 宋策, 等. 基于角点增强改进的TLD目标跟踪算法[J]. 液晶与显示, 2016, 31(9): 921-928.

    Sun B J. Zhang B, song C, et al. Improved TLD object tracking algorithm based on corner reinforced[J]. Chinese Journal of Liquid Crystals and Displays, 2016, 31(9): 921-928.

[9] Shi H, Lin Z, Tang W, et al. A robust hand tracking approach based on modified tracking-learning-detection algorithm[J]. Lecture Notes in Electrical Engineering, 2014, 308: 9-15.

[10] 祝贤坦, 石繁槐. 基于BRISK特征点改进的跟踪学习检测方法[J]. 计算机工程, 2017, 43(2): 268-272.

    Zhu X T, Shi F H. Improved tracking learning detection method based on BRISK keypoints[J]. Computer Engineering, 2017, 43(2): 268-272.

[11] 孟煜, 张斌. 检测区域自适应调整的TLD多目标跟踪算法[J]. 东北大学学报(自然科学版), 2017, 38(2): 214-218.

    Meng Y, Zhang B. A multiple-object tracking algorithm using TLD-based adaptive adjustment of detection areas[J]. Journal of Northeastern University (Natural Science Edition), 2017, 38(2): 214-218.

[12] 陆兵, 顾苏杭. 基于隐马尔可夫模型和分块特征匹配的目标跟踪算法[J]. 激光与光电子学进展, 2017, 54(9): 091006.

    Lu B, Gu S H. Object tracking algorithm based on hidden Markov model and block feature matching[J]. Laser & Optoelectronics Progress, 2017, 54(9): 091006.

[13] 周鑫, 钱秋朦, 叶永强, 等. 改进后的TLD视频目标跟踪方法[J]. 中国图象图形学报, 2013, 18(9): 1115-1123.

    Zhou X, Qian Q M, Ye Y Q, et al. Improved TLD visual target tracking algorithm[J]. Journal of Image and Graphics, 2013, 18(9): 1115-1123.

[14] 蔡荣太, 朱鹏. 基于马尔科夫随机场的多特征人脸跟踪算法[J]. 激光与光电子学进展, 2017, 54(2): 021002.

    Cai R T, Zhu P. Face tracking with multi-feature based on Markov random field[J]. Laser & Optoelectronics Progress, 2017, 54(2): 021002.

[15] Huang C, Li Y, Nevatia R. Multiple target tracking by learning-based hierarchical association of detection responses[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013, 35(4): 898-910.

[16] 周治平, 周明珠, 李文慧. 基于混合粒子滤波和稀疏表示的目标跟踪算法[J]. 模式识别与人工智能, 2016, 29(1): 22-30.

    Kang Z, Landry S J. Using scanpaths as a learning method for a conflict detection task of multiple target tracking[J]. Pattern Recognition and Artificial Intelligence, 2014, 56(6): 1150-1162.

[17] 张博, 龙慧. 基于图像签名算法的视觉目标跟踪算法[J]. 激光与光电子学进展, 2017, 54(9): 091504.

    Zhang B, Long H. Visual target tracking algorithm based on image signature algorithm[J]. Laser & Optoelectronics Progress, 2017, 54(9): 091504.

[18] Zhou Z P, Zhou M Z, Li W H. Object tracking algorithm based on hybrid particle filter and sparse representation[J]. Pattern Recognition and Artificial Intelligence, 2016, 29(1): 22-30.

[19] HareS, SaffariA, Torr P H S. Struck:structured output tracking with kernels[C]. IEEE International Conference on Computer Vision, 2012: 263- 270.