|
|
- 2018
基于自适应同时稀疏表示的鲁棒性目标追踪
|
Abstract:
综合考虑高斯噪声和拉普拉斯噪声，并通过拉普拉斯噪声的能量大小自适应的选择稀疏模型，该文提出了基于同时稀疏表示的自适应追踪算法。该算法可以更好的解决目标遮挡、姿势改变、光照变化和背景混杂等追踪问题，且具有更强的鲁棒性。其次提出一种基于子空间学习和无监督学习（K-means）相结合的模板更新方法，该方法一方面可以及时有效地反应目标的状态，另一方面也可以避免模板更新过快而引入较大的误差。然后，利用LASSO算法对该模型做了进一步的改进，并将目前较好的9种追踪算法与该文提出的算法进行比较，实验结果表明该算法在鲁棒性、精确性和实时性方面都得到了较好的改善。
| [1] | 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. |
| [2] | MAIRAL J, BACH F, PONCE J, et al. Online dictionary learning for sparse coding[C]//International Conference on Machine Learning. Quebec:DBLP, 2009. |
| [3] | LIWICKI S, ZAFEIRIOU S, TZIMIROPOULOS G, et al. Efficient online subspace learning with an indefinite kernel for visual tracking and recognition[J]. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(10):1624-1636. |
| [4] | YANG F, LU H, YANG M H. Robust superpixel tracking[J]. IEEE Transactions on Image Processing, 2014, 23(4):1639-1651. |
| [5] | HENRIQUES J F, CASEIRO R, MARTINS P, et al. Exploiting the circulant structure of tracking by detection with kernels[C]//Computer Vision-ECCV.[S.l.]:Springer, 2012, 7575:702-715. |
| [6] | MEI X, LING H. Robust visual tracking and vehicle classification via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(11):2259-2272. |
| [7] | LI Y, HE Z, YI S, et al. The robust patches-based tracking method via sparse representation[C]//International Conference on Security, Pattern Analysis, and Cybernetics. Wuhan:IEEE, 2014. |
| [8] | DUAN X, LIU J, TANG X. Visual tracking via weighted sparse representation[C]//International Conference on Intelligent Computing and Internet of Things. Harbin:IEEE, 2015. |
| [9] | LU X, YAO H, SUN X, et al. Real-time visual tracking using L2 norm regularization based collaborative representation[C]//IEEE International Conference on Image Processing. Melbourne:IEEE, 2013. |
| [10] | MEI X, LING H. Robust visual tracking using L1 minimization[C]//IEEE 12th International Conference on Computer Vision. Kyoto:DBLP, 2009. |
| [11] | WANG D, LU H, YANG M H. Online object tracking with sparse prototypes[J]. IEEE Transactions on Image Processing, 2013, 22(1):314-325. |
| [12] | LI A, LIN M, WU Y, et al. NUS-PRO:a new visual tracking challenge[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2016, 38(2):335-349. |
| [13] | YANG H, SHAO L, ZHENG F, et al. Recent advances and trends in visual tracking:a review[J]. Neurocomputing, 2011, 74(18):3823-3831. |
| [14] | 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. |
| [15] | SUN B, LIU Z, SUN Y, et al. Multiple objects tracking and identification based on sparse representation in surveillance video[C]//IEEE International Conference on Multimedia Big Data. Beijing:IEEE Computer Society, 2015. |
| [16] | WANG D, LU H, BO C. Online visual tracking via two view sparse representation[J]. IEEE Signal Processing Letters, 2014, 21(9):1031-1034. |
| [17] | BAO C, WU Y, LING H, et al. Real time robust L1 tracker using accelerated proximal gradient approach[C]//IEEE Conference on Computer Vision and Pattern Recognition. Providence:IEEE, 2012. |
| [18] | AHUJA N. Robust visual tracking via multi-task sparse learning[C]//Computer Vision and Pattern Recognition. Providence:IEEE, 2012. |
| [19] | YAN Q, LI L, WANG C, et al. Kernel sparse representation for object tracking[C]//International Conference on Multimedia Information Networking and Security. Beijing:IET, 2013. |
| [20] | WANG L, YAN H, LV K, et al. Visual tracking via kernel sparse representation with multikernel fusion[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(7):1132-1141. |
| [21] | ZHONG W, LU H, YANG M H. Robust object tracking via sparse collaborative appearance model[J]. IEEE Transactions on Image Processing, 2014, 23(5):2356-2368. |
| [22] | ADAM A, RIVLIN E, SHIMSHONI I. Robust fragments-based tracking using the integral histogram[J]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, 1:798-805. |
| [23] | ROSS D A, LIM J, LIN R S, et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(1-3):125-141. |
| [24] | WU Y, LIM J, YANG M H. Online object tracking:a benchmark[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Portland:IEEE, 2013. |
| [25] | LEARNEDMILLER E, SEVILLALARA L. Distribution fields for tracking[C]//Computer Vision and Pattern Recognition. Providence:IEEE, 2012. |
| [26] | HE J, ZHANG D, BALZANO L, et al. Iterative Grassmannian optimization for robust image alignment[J]. Image and Vision Computing, 2014, 32(10):800-813. |
