Robust tracking via weakly supervised ranking SVM

Appearance model is a key component of tracking algorithms. Most existing approaches utilize the object information contained in the current and previous frames to construct the object appearance model and locate the object with the model in frame t + 1. This method may work well if the object appearance just fluctuates in short time intervals. Nevertheless, suboptimal locations will be generated in frame t + 1 if the visual appearance changes substantially from the model. Then, continuous changes would accumulate errors and finally result in a tracking failure. To copy with this problem, in this paper we propose a novel algorithm - online Laplacian ranking support vector tracker (LRSVT) - to robustly locate the object. The LRSVT incorporates the labeled information of the object in the initial and the latest frames to resist the occlusion and adapt to the fluctuation of the visual appearance, and the weakly labeled information from frame t + 1 to adapt to substantial changes of the appearance. Extensive experiments on public benchmark sequences show the superior performance of LRSVT over some state-of-the-art tracking algorithms.

[1]  Junseok Kwon,et al.  Visual tracking decomposition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Horst Bischof,et al.  On-line semi-supervised multiple-instance boosting , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[4]  Alexander Zien,et al.  Semi-Supervised Learning , 2006 .

[5]  Klaus Obermayer,et al.  Support vector learning for ordinal regression , 1999 .

[6]  Tie-Yan Liu,et al.  Learning to rank: from pairwise approach to listwise approach , 2007, ICML '07.

[7]  Ming-Hsuan Yang,et al.  Visual tracking with online Multiple Instance Learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Xiaojin Zhu,et al.  Semi-Supervised Learning Literature Survey , 2005 .

[9]  Ming Yang,et al.  Tracking Nonstationary Visual Appearances by Data-Driven Adaptation , 2009, IEEE Transactions on Image Processing.

[10]  Gregory Hager,et al.  Multiple kernel tracking with SSD , 2004, CVPR 2004.

[11]  Jiri Matas,et al.  P-N learning: Bootstrapping binary classifiers by structural constraints , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Junzhou Huang,et al.  Robust tracking using local sparse appearance model and K-selection , 2011, CVPR 2011.

[13]  Horst Bischof,et al.  Semi-supervised On-Line Boosting for Robust Tracking , 2008, ECCV.

[14]  Eyke Hüllermeier,et al.  Preference Learning , 2005, Künstliche Intell..

[15]  Eyke Hllermeier,et al.  Preference Learning , 2010 .

[16]  Paul A. Viola,et al.  Multiple Instance Boosting for Object Detection , 2005, NIPS.

[17]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[18]  Ming-Hsuan Yang,et al.  Incremental Learning for Robust Visual Tracking , 2008, International Journal of Computer Vision.

[19]  Qingshan Liu,et al.  RankBoost with l1 regularization for facial expression recognition and intensity estimation , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[20]  Yihong Gong,et al.  Locality-constrained Linear Coding for image classification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  Tie-Yan Liu,et al.  Learning to Rank for Information Retrieval , 2011 .

[22]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Ming Tang,et al.  Robust visual tracking via ranking SVM , 2011, 2011 18th IEEE International Conference on Image Processing.

[24]  Zhuowen Tu,et al.  Feature Mining for Image Classification , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Yoram Singer,et al.  An Efficient Boosting Algorithm for Combining Preferences by , 2013 .

[26]  Ehud Rivlin,et al.  Robust Fragments-based Tracking using the Integral Histogram , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[27]  Horst Bischof,et al.  Real-Time Tracking via On-line Boosting , 2006, BMVC.

[28]  Tie-Yan Liu,et al.  Listwise approach to learning to rank: theory and algorithm , 2008, ICML '08.

[29]  Rong Yan,et al.  Imbalanced RankBoost for efficiently ranking large-scale image/video collections , 2009, CVPR.

[30]  Haibin Ling,et al.  Robust Visual Tracking using 1 Minimization , 2009 .

[31]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[32]  Horst Bischof,et al.  PROST: Parallel robust online simple tracking , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[33]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[34]  Haibin Ling,et al.  Robust visual tracking using ℓ1 minimization , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[35]  Jonathan Warrell,et al.  Proposal generation for object detection using cascaded ranking SVMs , 2011, CVPR 2011.

[36]  Qingshan Liu,et al.  Image retrieval via probabilistic hypergraph ranking , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[37]  Horst Bischof,et al.  On-line Random Forests , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.