Adaptive probabilistic tracking with multiple cues integration for a mobile robot

Visual tracking has been widely used in robot systems, and numerous approaches for visual tracking have been proposed. However, developing a robust and real-time visual tracking algorithm which can adaptively track the varying appearance of target under challenging conditions for mobile robot is still an open problem. This paper presents an adaptive probabilistic tracking algorithm with multiple cues integration. An effective evaluation function is proposed to evaluate each cue used for tracking based on their discriminating abilities between foreground and background. Then the likelihood functions of the cues are integrated in particle filter framework with different weights determined based on the evaluation scores. A novel target model updating strategy is proposed to adapt to the varying appearance of target resisting gradual drift which is still an unsolved problem in many adaptive tracking algorithms. Experimental results on a mobile robot demonstrate the robust performance of the proposed algorithm under challenging conditions.

[1]  Bohyung Han,et al.  Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Rudolph van der Merwe,et al.  The Unscented Kalman Filter , 2002 .

[3]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

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

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

[6]  Tao Xiong,et al.  Stochastic car tracking with line- and color-based features , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[7]  Patrick Pérez,et al.  Data fusion for visual tracking with particles , 2004, Proceedings of the IEEE.

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

[9]  David Schreiber,et al.  Robust template tracking with drift correction , 2007, Pattern Recognit. Lett..

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

[11]  Thomas S. Huang,et al.  Online updating appearance generative mixture model for meanshift tracking , 2007, Machine Vision and Applications.

[12]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

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

[14]  Yasushi Yagi,et al.  Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking , 2008, IEEE Transactions on Image Processing.

[15]  Emilio Maggio,et al.  Adaptive Multifeature Tracking in a Particle Filtering Framework , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[16]  David Suter,et al.  Adaptive Object Tracking Based on an Effective Appearance Filter , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Bernt Schiele,et al.  Towards Robust Multi-cue Integration for Visual Tracking , 2001, ICVS.

[18]  Anton van den Hengel,et al.  Probabilistic Multiple Cue Integration for Particle Filter Based Tracking , 2003, DICTA.

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

[20]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[21]  Francesc Moreno-Noguer,et al.  Dependent Multiple Cue Integration for Robust Tracking , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Peng Wang,et al.  Adaptive probabilistic tracking with reliable particle selection , 2009 .