Visual Tracking via Online Nonnegative Matrix Factorization

In visual tracking, holistic and part-based representations are both popular choices to model target appearance. The former is known for great efficiency and convenience, while the latter for robustness against local appearance or shape variations. Based on nonnegative matrix factorization (NMF), we propose a novel visual tracker that takes advantage of both groups. The idea is to model the target appearance by a nonnegative combination of nonnegative components learned from examples observed in previous frames. To adjust NMF to the tracking context, we include sparsity and smoothness constraints in addition to the nonnegativity one. Furthermore, an online iterative learning algorithm, together with a proof of convergence, is proposed for efficient model updating. Putting these ingredients together with a particle filter framework, the proposed tracker, constrained online nonnegative matrix factorization (CONMF), achieves robustness to challenging appearance variations and nontrivial deformations while running in real time. We evaluate the proposed tracker on various benchmark sequences containing targets undergoing large variations in scale, pose, or illumination. The robustness and efficiency of CONMF is validated in comparison with several state-of-the-art trackers.

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

[2]  Shai Avidan,et al.  Ensemble Tracking , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[4]  Robert T. Collins,et al.  An Open Source Tracking Testbed and Evaluation Web Site , 2005 .

[5]  Hyunsoo Kim,et al.  Sparse Non-negative Matrix Factorizations via Alternating Non-negativity-constrained Least Squares , 2006 .

[6]  Stanley T. Birchfield,et al.  Adaptive fragments-based tracking of non-rigid objects using level sets , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[7]  Hujun Bao,et al.  Understanding the Power of Clause Learning , 2009, IJCAI.

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

[9]  Patrick Pérez,et al.  Color-Based Probabilistic Tracking , 2002, ECCV.

[10]  Gregory D. Hager,et al.  Efficient Region Tracking With Parametric Models of Geometry and Illumination , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Huchuan Lu,et al.  Superpixel tracking , 2011, 2011 International Conference on Computer Vision.

[12]  Luo Si,et al.  Non-Negative Matrix Factorization Clustering on Multiple Manifolds , 2010, AAAI.

[13]  Zhongfei Zhang,et al.  A survey of appearance models in visual object tracking , 2013, ACM Trans. Intell. Syst. Technol..

[14]  Junseok Kwon,et al.  Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive Basin Hopping Monte Carlo sampling , 2009, CVPR.

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

[16]  Takahiro Ishikawa,et al.  The template update problem , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Michael J. Black,et al.  An Adaptive Appearance Model Approach for Model-based Articulated Object Tracking , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[18]  Junseok Kwon,et al.  Tracking by Sampling Trackers , 2011, 2011 International Conference on Computer Vision.

[19]  David J. Fleet,et al.  Robust online appearance models for visual tracking , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

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

[22]  Yi Wu,et al.  Online Object Tracking: A Benchmark , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Erik Blasch,et al.  Minimum Error Bounded Efficient L1 Tracker with Occlusion Detection (PREPRINT) , 2011 .

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

[25]  Hanqing Lu,et al.  Real-time visual tracking via Incremental Covariance Tensor Learning , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[26]  Feng Li,et al.  Blurred target tracking by Blur-driven Tracker , 2011, 2011 International Conference on Computer Vision.

[27]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[28]  Li Bai,et al.  Efficient Minimum Error Bounded Particle Resampling L1 Tracker With Occlusion Detection , 2013, IEEE Transactions on Image Processing.

[29]  Jérôme Idier,et al.  Algorithms for Nonnegative Matrix Factorization with the β-Divergence , 2010, Neural Computation.

[30]  Serhat Selcuk Bucak,et al.  Incremental subspace learning via non-negative matrix factorization , 2009, Pattern Recognit..

[31]  Huchuan Lu,et al.  Robust object tracking via sparsity-based collaborative model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Ahmed M. Elgammal,et al.  Modeling View and Posture Manifolds for Tracking , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[33]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[34]  David J. Fleet,et al.  Robust Online Appearance Models for Visual Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Stanley T. Birchfield,et al.  Elliptical head tracking using intensity gradients and color histograms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[36]  Li Bai,et al.  Real-Time Probabilistic Covariance Tracking With Efficient Model Update , 2012, IEEE Transactions on Image Processing.

[37]  Haibin Ling,et al.  Real time robust L1 tracker using accelerated proximal gradient approach , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

[39]  Hanzi Wang,et al.  Generalized Kernel-Based Visual Tracking , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[40]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[41]  Gregory D. Hager,et al.  A Nonparametric Treatment for Location/Segmentation Based Visual Tracking , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[42]  Horst Bischof,et al.  MIForests: Multiple-Instance Learning with Randomized Trees , 2010, ECCV.

[43]  Qing Wang,et al.  Online discriminative object tracking with local sparse representation , 2012, 2012 IEEE Workshop on the Applications of Computer Vision (WACV).

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

[45]  Bin Shen,et al.  Online robust image alignment via iterative convex optimization , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[46]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[47]  Qiang Yang,et al.  Detect and Track Latent Factors with Online Nonnegative Matrix Factorization , 2007, IJCAI.

[48]  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).