Object tracking based on the joint model using L2-norm minimization
-
摘要: 为了解决稀疏表示的跟踪算法的计算代价比较大,且目标的表观由于多种原因会发生变化的问题,提出了一种在贝叶斯推理框架下,建立结合基于全局模板的判别式模型和基于局部描述子的生成式模型的联合模型,通过L2范数最小化进行求解的目标跟踪方法.在跟踪过程中,适时地更新判别式模型中的正负模板和生成式模型中模板的系数向量,使模板具有很强的适应性和判别性.实验结果表明,与其他典型的算法相比,该算法对于光照变化、尺度变化、遮挡、旋转等情况具有较强的鲁棒性.Abstract: The computational cost of the tracking algorithm based on the sparse representation is so much large, at the same time, the target apparence changes on account of a variety of reasons,which makes the object tracking process complicated and time consuming. A joint model is reasonably proposed by combining the global template based on the discriminant model and the generation model based on the local descriptor, properly solved by the L2-norm minimization solution in a bayesian inference framework, which is proved to be effective and efficient. In the process of the object tracking process, the plus template and the minus template of the discriminant model and the coefficient vector of the generative model are timely updated so as to have a strong adaptability and robust discrimination. The experimental results finally show that compared with other typical algorithms, the proposed algorithm has stronger robustness in the case of illumination, scale changes, shelter, rotation and so on.
-
Key words:
- object tracking /
- L2-norm minimization /
- discriminative model /
- generative model /
- subspace
-
[1] Wu Y, Lim J,Yang M H.Online object tracking:a benchmark[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington,DC:IEEE Computer Society,2013:2411-2418. [2] 邵文坤,黄爱民, 韦庆.目标跟踪方法综述[J].影像技术,2006(1):17-20. Shao W K,Huang A M,Wei Q.Target tracking method review[J].Image Technology,2006(1):17-20(in Chinese). [3] Zhong W, Lu H,Yang M H.Robust object tracking via sparsity-based collaborative model[C]//Proc IEEE Comput Soc Conf Comput Vision Pattern Recognition.Washington,DC:IEEE Computer Society,2012:1838-1845. [4] 沈丁成,薛彦兵, 张桦,等.一种鲁棒的基于在线boosting目标跟踪算法研究[J].光电子·激光,2013,24(11):30. Shen D C,Xue Y B,Zhang H,et al.A robust online boosting target tracking algorithm based on the research[J].Journal of Photoelectron·Laser,2013,24(11):30(in Chinese). [5] Grabner H, Grabner M,Bischof H.Real-time tracking via on-line boosting[C]//BMVC 2006-Proceedings of the British Machine Vision Conference 2006.Edinburgh:British Machine Vision Association,2006:47-56. [6] 张颖颖,王红娟, 黄义定.基于在线多实例学习的跟踪研究[J].南阳师范学院学报,2012,10(12):35-37. Zhang Y Y,Wang H J,Huang Y D.Based on multiple instance learning online tracking study[J].Journal of Nanyang Normal University,2012,10(12):35-37(in Chinese). [7] Babenko B, Belongie S,Yang M H.Visual tracking with online multiple instance learning[C]//2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.Piscataway,NJ:IEEE Computer Society,2009:983-990. [8] Avidan S. Ensemble tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(2):261-271. [9] Ross D, Lim J,Lin R S,et al.Incremental learning for robust visual tracking[J].International Journal of Computer Vision,2008,77(1):125-141. [10] 齐飞,罗予频, 胡东成.基于均值漂移的视觉目标跟踪方法综述[J].计算机工程,2007,33(21):24-27. Qi F,Luo Y P,Hu D C.Visual target tracking method based on mean shift review[J].Computer Engineering,2007,33(21):24-27(in Chinese). [11] Black M,Jepson A. Eigentracking:robust maching and tracking of articulated objects using a view based representation[J].International Journal of Computer Vision,1998,26(1):63-84. [12] Yang A Y, Sastry S S,Ganesh A,et al.Fast 1 -minimization algorithms and an application in robust face recognition:a review[C]//Image Processing.Hong Kong:IEEE,2010:1849-1852. [13] Wright J, Yang A Y,Ganesh A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227. [14] Mei X, Ling H.Robust visual tracking using L1 minimization[C]//Computer Vision.Anchorage,Alaska:IEEE,2009:1436-1443. [15] Mei X, Ling H,Wu Y,et al.Minimum error bounded efficientl1 tracker with occlusion detection[C]//Computer Vision and Pattern Recognition.Colorado Springs:IEEE,2011:1257-1264. [16] Bao C L, Wu Y,Ling H,et al.Real time robust l1 tracker using accelerated proximal gradient approach[C]//Proc IEEE Comput Soc Conf Comput Vision Pattern Recognition.Washington,DC:IEEE Computer Society,2012:1830-1837. [17] Zhang T Z, Ghanem B,Liu S,et al.Robust visual tracking via multi-task sparse learning[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington,DC:IEEE Computer Society,2012:2042-2049. [18] Zhang D, Yang M,Feng X.Sparse representation or collaborative representation:which helps face recognition [C]//Computer Vision,2011:471-478. [19] Xiao Z Y, Lu H,Wang D.Object tracking with L2-RLS[C]//Proceedings-International Conference on Pattern Recognition.Piscataway,NJ:Institute of Electrical and Electronics Engineers Inc,2012:1351-1354. [20] Adam A, Rivlin E,Shimshoni I.Robust fragments-based tracking using the integral histogram[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.New York:Electronics Engineers Computer Society,2006:798-805. [21] Kwon J, Lee K M.Visual tracking decomposition[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE Computer Society,2010:1269-1276. [22] Maggio E, Cavallaro A.Hybrid particle filter and mean shift tracker with adaptive transition model[C]//ICASSP,IEEE International Conference on Acoustics,Speech and Signal Processing-Proceedings.Philadelphia,PA:Institute of Electrical and Electronics Engineers Inc,2005:221-224.
点击查看大图
计量
- 文章访问数: 1205
- HTML全文浏览量: 139
- PDF下载量: 757
- 被引次数: 0