Appearance Modeling for Visual Tracking Shaohua

Visual tracking needs modeling inter-frame motion and appearance changes. In conventional visual tracking algorithms, the appearance model is either fixed or rapidly changing, and the motion model is simply a random walk with fixed noise variance. Also, if particle filter is used for solving tracking problem, the the number of particles is typically fixed. All these factors make the visual tracker unstable. To stabilize the tracker, we propose the following modifications: an observation model arising from an adaptive appearance model, an adaptive velocity motion model with adaptive noise variance, and an adaptive number of particles. The adaptive-velocity model is derived using a first-order linear predictor based on the appearance difference between the incoming observation and the previous particle configuration. Experimental results on tracking visual objects in long outdoor and indoor video sequences demonstrate the effectiveness and robustness of our tracking algorithm. We also present extensions of handling occlusion in a monocular sequence and sequences captures by two wide-baseline cameras.

[1]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

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

[3]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[4]  P. Anandan,et al.  Hierarchical Model-Based Motion Estimation , 1992, ECCV.

[5]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[6]  Petros G. Voulgaris,et al.  On optimal ℓ∞ to ℓ∞ filtering , 1995, Autom..

[7]  Alex Pentland,et al.  Recursive Estimation of Motion, Structure, and Focal Length , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[9]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[10]  Michael J. Black,et al.  A probabilistic frameworkfor matching temporal trajectories , 1998, ICCV 1998.

[11]  Michael Isard,et al.  ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework , 1998, ECCV.

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

[13]  David J. Fleet,et al.  Probabilistic detection and tracking of motion discontinuities , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[15]  David J. Fleet,et al.  Stochastic Tracking of 3D Human Figures Using 2D Image Motion , 2000, ECCV.

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

[17]  Shaogang Gong,et al.  Tracking Multiple People Under Occlusion Using Multiple Cameras , 2000, BMVC.

[18]  Michael Isard,et al.  Learning and Classification of Complex Dynamics , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Michel Dhome,et al.  A simple and efficient template matching algorithm , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[20]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[21]  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.

[22]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[23]  Andrew Blake,et al.  Probabilistic tracking in a metric space , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[24]  Gregory D. Hager,et al.  Probabilistic Data Association Methods for Tracking Complex Visual Objects , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  R Chellappa,et al.  Face verification through tracking facial features. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[26]  Ying Wu,et al.  A co-inference approach to robust visual tracking , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[27]  Dieter Fox,et al.  KLD-Sampling: Adaptive Particle Filters and Mobile Robot Localization , 2001, NIPS 2001.

[28]  A. M. Tekalp,et al.  Multiple camera fusion for multi-object tracking , 2001, Proceedings 2001 IEEE Workshop on Multi-Object Tracking.

[29]  Rama Chellappa,et al.  Probabilistic recognition of human faces from video , 2002, Proceedings. International Conference on Image Processing.

[30]  James Black,et al.  Multi view image surveillance and tracking , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[31]  Patrick Pérez,et al.  Towards Improved Observation Models for Visual Tracking: Selective Adaptation , 2002, ECCV.

[32]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[33]  Gang Hua,et al.  Tracking appearances with occlusions , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

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

[35]  B. Ripley,et al.  Robust Statistics , 2018, Wiley Series in Probability and Statistics.

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

[37]  Sharath Pankanti,et al.  Appearance models for occlusion handling , 2006, Image Vis. Comput..