全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

加权局部特征结合判别式字典的目标跟踪

DOI: 10.11834/jig.20140908

Keywords: 判别式字典,局部特征,稀疏系数,稀疏表示,相似性度量

Full-Text   Cite this paper   Add to My Lib

Abstract:

目的当前大多数基于稀疏表示的跟踪方法只考虑全局特征或局部特征的最小重构误差,没有充分利用稀疏编码系数,或者忽略了字典判别性的作用,尤其当目标被相似物遮挡时,往往会导致跟踪目标丢失。针对上述问题,提出一种新的基于判别式字典和加权局部特征的稀疏外观模型(SPAM-DDWF)跟踪算法。方法首先利用Fisher准则学习判别式字典,对提取的局部特征进行结构性分析来区分目标和背景,其次,提出一种新的基于加权的相似性度量方法来处理遮挡问题,从而提高跟踪的精确度。此外,基于重构系数的权重更新策略,使算法能更好地适应跟踪目标的外观变化,并降低了遮挡发生时跟踪漂移的概率。结果在多个基准图像序列上,与多种流行方法对比,本文算法在光照变化、复杂背景、遮挡等场景中保持较高的跟踪成功率与较低的漂移误差。平均成功率和漂移误差分别为76.8%和3.7。结论实验结果表明,本文算法具有较好的有效性和鲁棒性,尤其在目标被相似物遮挡的情况下,也能较准确地跟踪到目标。

References

[1]  Avidan S. Ensemble tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(2):261-271.
[2]  更多...
[3]  Mairal J, Bach F, Ponce J, et al. Online learning for matrix factorization and sparse coding [J]. The Journal of Machine Learning Research, 2010, 11:19-60.
[4]  Everingham M, Gool L V, Williams C K I, et al. The Pascal visual object classes (voc) challenge [J]. International Journal of Computer Vision, 2010, 88(2):303-338.
[5]  Grabner H, Bischof H. On-line boosting and vision [C]// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, NY, USA: IEEE Computer Society, 2006:260-267.
[6]  Grabner H, Leistner C, Bischof H. Semi-supervised on-line boosting for robust tracking [C]// Proceedings of the 10th European Conferences on Computer Vision. Marseille, France: Springer, 2008: 234-247.
[7]  Babenko B, Yang M H, Belongie S. Visual tracking with online multiple instance learning [C]// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Miami, Florida, USA: IEEE Computer Society, 2009: 983-990.
[8]  Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision [C]// Proceedings of the 7th International Joint Conference on Artificial Intelligence. Vancouver, British Columbia, Canada:William Kaufmann, 1981:674-679.
[9]  Yang F, Lu H, Zhang W, et al. Visual tracking via bag of features [J]. IET Image Process, 2012, 6(2):115-128.
[10]  Matthews I, Ishikawa T, Baker S. The template update problem [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(6):810-815.
[11]  Kwon J, Lee K M. Visual tracking decomposition [C]// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. California, USA: IEEE Computer Society,2010:1269-1276.
[12]  Black M J, Jepson A D. Eigentracking: robust matching and tracking of articulated objects using a view-based representation [J]. The Journal of Machine Learning Research, 1988, 26(1):163-84,1988.
[13]  Ross D A, Lim J, Lin R S, et al. Incremental learning for robust visual tracking [J]. The Journal of Machine Learning Research, 2008, 77(1): 125-141.
[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]  Mei X, Ling H. Robust visual tracking using L1 minimization [C]// Proceedings of IEEE International Conference on Computer Vision. Kyoto Japan: IEEE, 2009:1436-1443.
[16]  Liu B Y, Yang L, Huang J Z, et al. Robust and fast collaborative tracking with two stage sparse optimization [C]// Proceedings of the 11th European Conference on Computer Vision. Crete Greece: Springer, 2010:624-637.
[17]  Mei X, Ling H B, Wu Y, et al. Minimum error bounded efficient L1 tracker with occlusion detection [C]// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Colorado Springs, USA: IEEE Computer Society, 2011:1257-1264.
[18]  Liu B Y, Huang J Z, Yang, et al. Robust tracking using local sparse appearance model and k-selection [C]// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Colorado Springs, USA: IEEE Computer Society, 2011:1313-1320.
[19]  Jia X, Lu H C, Yang M H. Visual tracking via adaptive structural local sparse appearance model [C]// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Providence, Rhode Island, USA: IEEE Computer Society, 2012:1822-1829.
[20]  Zhong W, Lu H C, Yang M H. Robust Object Tracking via Sparsity-based Collaborative Model [C]// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Providence, Rhode Island, USA: IEEE Computer Society, 2012:1838-1845.
[21]  Jiang Z L, Lin Z, Davis L S. Learning a discriminative dictionary for sparse coding via label consistent K-SVD [C]// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Colorado Springs, USA: IEEE Computer Society, 2011:1697-1704.
[22]  Zhang Q, Li B X. Discriminative K-SVD for dictionary learning in face recognition [C]// Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. California, USA: IEEE Computer Society, 2010:2691-2698.
[23]  Yang M, Zhang L, Feng X C, et al. Fisher discrimination dictionary learning for sparse representation [C]// Proceedings of IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011:543-550.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133