Visual Object Tracking Based on Backward Model Validation

Appearance model updating is a challenging task in visual object tracking with occlusion and appearance variation. To avoid error accumulation in model updating, validation of updating is generally performed in tracking algorithms. These algorithms use the existing appearance model to validate incoming data. However, the existing appearance model may not be able to distinguish the valid training data (resulting from large appearance variation) from the invalid ones (resulting from occlusion), since both appearance variation and occlusion would lead to a good deal of appearance change of the estimated tracking result. The root of the problem is: the existing (outdated) model with information from frame 1 to n-1 may not be able to predict large appearance variations in frame n and, as a result, the appearance variations may be excluded from model updating. This defeats the purpose of model updating, which is to include new changes in appearance variations to the model, because the existing methods do not have the provision to include such changes in model updating by validating changes with the outdated model. To address this problem, we propose a backward model validation-based visual tracking (BVT) algorithm, which performs model updating first in frame n and then uses the information from the incoming frame (frame n + 1) to backward-check whether the updating is valid (occurrence of appearance variation) or invalid (occurrence of occlusion). In this way, the uncertainty of validating unpredictable features with the existing appearance models can be avoided. Moreover, an adaptive feature fusion method is designed to properly integrate the color-based feature with texture-based feature. The proposed feature extraction method provides a robust representation of the target with both rotation and shape deformation. Experimental results demonstrate that the proposed BVT algorithm outperforms the relevant existing algorithms on both publicly available and proprietary databases.

[1]  Branko Ristic,et al.  A particle filter for joint detection and tracking of color objects , 2007, Image Vis. Comput..

[2]  Cor J. Veenman,et al.  Resolving Motion Correspondence for Densely Moving Points , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

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

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

[5]  J. Ferryman,et al.  PETS2009: Dataset and challenge , 2009, 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance.

[6]  Gérard G. Medioni,et al.  Online Tracking and Reacquisition Using Co-trained Generative and Discriminative Trackers , 2008, ECCV.

[7]  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).

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

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

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

[11]  Michael J. Black,et al.  EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation , 1996, International Journal of Computer Vision.

[12]  Ying Wu,et al.  Contextual flow , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

[14]  Sam T. Roweis,et al.  EM Algorithms for PCA and SPCA , 1997, NIPS.

[15]  Patrick Pérez,et al.  An adaptive mixture color model for robust visual tracking , 2006, 2006 International Conference on Image Processing.

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

[17]  Narendra Ahuja,et al.  Robust visual tracking via multi-task sparse learning , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  N. Ahuja,et al.  Robust Visual Tracking via MultiTask Sparse Learning , 2012 .

[19]  Luc Van Gool,et al.  Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  James J. Little,et al.  A Boosted Particle Filter: Multitarget Detection and Tracking , 2004, ECCV.

[21]  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).

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

[23]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

[25]  Volkan Cevher,et al.  Target Tracking Using a Joint Acoustic Video System , 2007, IEEE Transactions on Multimedia.

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

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

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

[29]  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).

[30]  Luc Van Gool,et al.  An adaptive color-based particle filter , 2003, Image Vis. Comput..

[31]  Mohan S. Kankanhalli,et al.  Pedestrian Tracking Based on Hidden-Latent Temporal Markov Chain , 2011, MMM.

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

[33]  Yuan Xie,et al.  Online multiple instance gradient feature selection for robust visual tracking , 2012, Pattern Recognit. Lett..

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

[35]  Zdenek Kalal,et al.  Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[37]  Afshin Dehghan,et al.  Part-based multiple-person tracking with partial occlusion handling , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[40]  Laura Sevilla-Lara,et al.  Distribution fields for tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[41]  Rynson W. H. Lau,et al.  Visual Tracking via Locality Sensitive Histograms , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[42]  Luc Van Gool,et al.  Depth and Appearance for Mobile Scene Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[43]  Rama Chellappa,et al.  Visual tracking and recognition using appearance-adaptive models in particle filters , 2004, IEEE Transactions on Image Processing.

[44]  Daniel P. Huttenlocher,et al.  Pictorial Structures for Object Recognition , 2004, International Journal of Computer Vision.

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

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