the distance from missile to the target and target's moving status and appearance change a lot. Using a single fixed template cannot achieve stable tracking. In this paper
a new subspace-based moving target tracking algorithm is proposed. First
a set of orthogonal sparse subspace feature vectors are used to describe target model. Then
using incremental method to continuously update the subspace model to accommodate the target changes in appearance which is caused by intrinsic and extrinsic factors
therefore
the tracking accuracy is improved. Finally
importance sampling algorithm and maximum likelihood estimation are applied to solve complex optimization problems. Experimental results show that when the camera and the background suffer larger relative motion and target status changed dramatically
the algorithm can still be able to achieve sustained and stable tracking
and the average tracking error keeps less than 10 pixels. It can satisfy the system requirements of stability and robustness for terminal guidance tracking.
关键词
Keywords
references
陈尚峰. 成像末制导目标检测与跟踪技术研究. 长沙:国防科学技术大学,2009. Chen C F. Research on targets detecting and tracking for image homing guidance. Changsha:National University of Defense Technology,2009. (in Chinese)[2] 朱秋平,颜佳,张虎,等. 基于压缩感知的多特征实时跟踪[J]. 光学 精密工程,2013,21(2):437-444. Zhu Q P,Yan J,Zhang H,et al. Real time tracking using multiple features based on compressive sensing [J]. Opt. Precision Eng.,2013,21(2):437-444. (in Chinese)[3] 贾平,徐宁,张叶. 基于局部特征提取的目标自动识别[J]. 光学 精密工程,2013,21(7):1898-1905. Jia P,Xu N,Zhang Y. Automatic target recognition based on local feature extraction [J]. Opt. Precision Enginee-ring,2013,21(7):1898-1905. (in Chinese)[4] Li J Y,Wang Y L,Wang Y J. Visual tracking and learning using speeded up robust features [J]. Pattern Recognition Letters,2012,33(16):2094-2101.[5] 王田,刘伟宁,韩广良,等. 基于改进MeanShift的目标跟踪算法 [J]. 液晶与显示,2012,27(3):396-400. Wang T,Liu W N,Han G L,et al. Target tracking algorithm based on improved Meanshift [J]. Chinese Journal of Liquid Crystals and Displays,2012,27(3):396-400. (in Chinese)[6] 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. Liu Y,Zhang Y F,Dong Y F. Anti-occlusion algorithm of tracking moving object in clutter background [J]. Chinese Journal of Liquid Crystals and Displays, 2010,25(6):890-895. (in Chinese)[7] Ross D A,Lim J,Lin R,et al. Incremental learning for robust visual tracking [J]. International Journal of Computer Vision,2008,77(1-3):125-141.[8] 葛微,程宇奇,刘春香,等. 基于子空间分析的人脸识别方法研究 [J]. 中国光学与应用光学,2009,2(5):377-387. Ge W,Cheng Y Q,Liu C X,et al. Research of face recognition methods base on subspace analysis [J]. Chinese Journal of Optics and Applied Optics,2009,2(5):377-387. (in Chinese)[9] 孙晓燕,常发亮. 梯度特征稀疏表示目标跟踪 [J]. 光学 精密工程,2013,21(12):3191-3197. Sun X Y,Chang F L. Object tracking based on sparse representation of gradient feature [J].Opt. Precision Eng.,2013,21(12):3191-3197. (in Chinese)[10] 李忠新. 图像镶嵌理论及若干算法研究.南京:南京理工大学,2004. Li Z X. On the theory of image mosaicing and its related algorithms. Nanjing:Nanjing University of Science and Technology,2004. (in Chinese)[11] 钱诚. 增量型目标跟踪关键技术研究.杭州:浙江大学,2011. Qian C. Research on key techniques of incremental object tracking. Hangzhou:Zhejiang University,2011. (in Chinese)[12] 杜超,刘伟宁,刘恋. 一种基于卡尔曼滤波及粒子滤波的目标跟踪算法 [J]. 液晶与显示,2011,26(3):384-389. Du C,Liu W N,Liu L. Target tracking algorithm based on kalman filter and particle filter [J]. Chinese Journal of Liquid Crystals and Displays, 2011,26(3):384-389. (in Chinese)