Camshift Guided Particle Filter for Visual Tracking

Particle filter and mean shift are two important methods for tracking object in video sequence, and they are extensively studied by researchers. As their strength complements each other, some effort has been initiated in [1] to combine these two algorithms, on which the advantage of computational efficiency is focused. In this paper, we extend this idea by exploring even more intrinsic relationship between mean shift and particle filter, and propose a new algorithm, CamShift guided particle filter (CAMSGPF). In CAMSGPF, two basic algorithms - CamShift and particle filter - can work cooperatively and benefit from each other, so that the overall performance is improved and some redundancy in algorithms can be removed. Experimental results show that the proposed method can track objects robustly in various environments, and is much faster than the existing methods.

[1]  Takayuki Okatani,et al.  Object tracking by the mean-shift of regional color distribution combined with the particle-filter algorithms , 2004, ICPR 2004.

[2]  Dorin Comaniciu,et al.  Mean shift and optimal prediction for efficient object tracking , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[3]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.

[4]  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).

[5]  Andrew Blake,et al.  A Probabilistic Exclusion Principle for Tracking Multiple Objects , 2004, International Journal of Computer Vision.

[6]  J. Hammersley,et al.  Poor Man's Monte Carlo , 1954 .

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

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

[9]  Emilio Maggio,et al.  Hybrid particle filter and mean shift tracker with adaptive transition model , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[10]  Tieniu Tan,et al.  Real time hand tracking by combining particle filtering and mean shift , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[11]  Andrew Blake,et al.  A Probabilistic Exclusion Principle for Tracking Multiple Objects , 2000, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[12]  Takayuki Okatani,et al.  Object tracking by the mean-shift of regional color distribution combined with the particle-filter algorithms , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[13]  Stuart J. Russell,et al.  Stochastic simulation algorithms for dynamic probabilistic networks , 1995, UAI.

[14]  Gary R. Bradski,et al.  Real time face and object tracking as a component of a perceptual user interface , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[15]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

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

[17]  James J. Little,et al.  Robust Visual Tracking for Multiple Targets , 2006, ECCV.

[18]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[19]  P. Fearnhead,et al.  Improved particle filter for nonlinear problems , 1999 .