Appearance Tracking Using Adaptive Models in a Particle Filter

The particle filter is a popular tool for visual tracking. Usually, the appearance model is either fixed or rapidly changing and the motion model is simply a random walk with fixed noise variance. Also, the number of particles used is typically fixed. All these factors make the visual tracker unstable. To stabilize the tracker, we propose the following measures: an observation model arising from an adaptive noise variance, and adaptive number of particles. The adaptivevelocity is computed via a first-order linear predictor using the previous particle configuration. Tracking under occlusion is accomplished using robust statistics. Experimental results on tracking visual objects in long video sequences such as vehicles, tank, and human faces demonstrate the effectiveness and robustness of our algorithm.

[1]  G. Casella,et al.  Statistical Inference , 2003, Encyclopedia of Social Network Analysis and Mining.

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

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

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

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

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

[7]  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.

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

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

[10]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

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

[12]  Rama Chellappa,et al.  Probabilistic Human Recognition from Video , 2002, ECCV.

[13]  Rama Chellappa,et al.  A robust algorithm for probabilistic human recognition from video , 2002, Object recognition supported by user interaction for service robots.

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

[15]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.