Robust tracking via online Max-Margin structural learning with approximate sparse intersection kernel

Abstract In this paper, we propose a robust visual tracking algorithm using online Max-Margin structural learning with sparse intersection kernel approximations to address the appearance variation, such as pose and scale variations, illumination changes, and occlusion. In the proposed tracking framework, we introduce a new sparse approximate HOG-based appearance model to represent the tracked object in high-dimensional feature space, which is efficient for measurement of non-linear intersection kernel. We then propose a optimisation method by extending Pegasos to the structural output with sparse intersection kernel in an online setting. Based on the new discriminative appearance models, we build a tracking algorithm using a Bayesian state inference framework to update the dynamic motion of object. The qualitative and quantitative experimental evaluations on 14 challenging video sequences demonstrate that the proposed tracking algorithm outperforms state-of-the-art trackers. We also show the low time complexity of the proposed tracker.

[1]  Gang Hua,et al.  Discriminative Tracking by Metric Learning , 2010, ECCV.

[2]  Yanxi Liu,et al.  Online selection of discriminative tracking features , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Anton van den Hengel,et al.  Non-sparse linear representations for visual tracking with online reservoir metric learning , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[5]  Gérard G. Medioni,et al.  Context tracker: Exploring supporters and distracters in unconstrained environments , 2011, CVPR 2011.

[6]  Shai Avidan,et al.  Support vector tracking , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Ming-Hsuan Yang,et al.  Robust Object Tracking with Online Multiple Instance Learning , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Haibin Ling,et al.  Robust Visual Tracking and Vehicle Classification via Sparse Representation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Andrew Zisserman,et al.  Sparse kernel approximations for efficient classification and detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Trevor Darrell,et al.  The pyramid match kernel: discriminative classification with sets of image features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[11]  Philip H. S. Torr,et al.  Struck: Structured output tracking with kernels , 2011, ICCV.

[12]  Zezhong Xu,et al.  Visual tracking with structural appearance model based on extended incremental non-negative matrix factorization , 2014, Neurocomputing.

[13]  Yanning Zhang,et al.  Robust Tracking with Weighted Online Structured Learning , 2012, ECCV.

[14]  Jason Weston,et al.  Solving multiclass support vector machines with LaRank , 2007, ICML '07.

[15]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[16]  Lu Zhang,et al.  Structure Preserving Object Tracking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

[18]  Christoph H. Lampert,et al.  Learning to Localize Objects with Structured Output Regression , 2008, ECCV.

[19]  Yoram Singer,et al.  Pegasos: primal estimated sub-gradient solver for SVM , 2007, ICML '07.

[20]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[21]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[22]  Subhransu Maji,et al.  Max-margin additive classifiers for detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[23]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[24]  Shai Avidan.  Ensemble Tracking , 2007, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  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).

[26]  Rongrong Ji,et al.  Robust tracking via patch-based appearance model and local background estimation , 2014, Neurocomputing.

[27]  Gang Hua,et al.  Context-Aware Visual Tracking , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[29]  Chunhua Shen,et al.  Real-time visual tracking using compressive sensing , 2011, CVPR 2011.

[30]  Lei Zhang,et al.  Real-Time Compressive Tracking , 2012, ECCV.

[31]  Thomas Hofmann,et al.  Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..

[32]  Antoine Bordes,et al.  Sequence Labelling SVMs Trained in One Pass , 2008, ECML/PKDD.

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

[34]  Yanxi Liu,et al.  Online Selection of Discriminative Tracking Features , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Ming-Hsuan Yang,et al.  Visual tracking with histograms and articulating blocks , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Olivier Chapelle,et al.  Training a Support Vector Machine in the Primal , 2007, Neural Computation.

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

[38]  Yanning Zhang,et al.  Part-Based Visual Tracking with Online Latent Structural Learning , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.