Efficient second-order minimization combined with fuzzy control-based visual tracking of a moving face

Abstract

This paper presents a visual feedback control system that is based on an efficient second-order minimization algorithm and can be used to track a moving face. This study aims to control a camera installed at the end of a three-degree-of-freedom robot arm and fix its orientation with respect to a moving face so that the face remains at a constant position in the image. In this work, the efficient second-order minimization algorithm is used to feed back the orientation of the two-dimensional human face. Then, an intelligent fuzzy algorithm is utilized by the tracking system to allow it to both track and become synchronized with the movement of the face. In addition, a Kalman filter is implemented to create an improved performance of the prediction of the position of the face. The applied control scheme is tested in tracking experiments on a moving face performed using a robot system with a camera installed on its end-effecter and the proposed scheme is shown to be an effective approach to the considered problem.

Keywords

Efficient second-order minimization image-based visual control visual tracking fuzzy control

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References

William

Hulls

CCW

Bell

. Relative end-effector control using Cartesian position based visual servoing. IEEE Trans Robot Autom 1996; 12: 684–696.

Xue

Kasper

Zoellner

. An automatic grasp planning system for service robots. In: International conference on advanced robotics, Munich, 2009, pp. 1–6. Piscataway, NJ: IEEE Press.

Hunt

Sanderson

. Vision-based predictive tracking of a moving target. Technical Report CMU-RI-TR-82-15, Carnegie Mellon University, USA, January 1982.

Luo

Mullen

Wessel

. An adaptive robotics tracking system using optical flow. In: IEEE international conference on robotics and automation, Philadelphia, USA, 1988, pp.568–573. Piscataway NJ: IEEE Press.

Chaumette

Tahri

. A shape tracking algorithm for visual servoing. In: IEEE international conference on robotics and automation, Barcelona, Spain, April 2005, pp. 2847–2852. Piscataway NJ: IEEE Press.

Tahri

Mezouar

. On visual servoing based on efficient second order minimization. Robot Auton Syst 2010; 58: 712–719.

Benhimane

Malis

. Homography-based 2D visual tracking and servoing. Int J Robot Res 2007; 26: 661–676.

Tung

Fan

. Application of fuzzy on-line self-adaptive controller for a contour tracking robot on unknown contours. Fuzzy Sets Syst 1996; 82: 17–25.

Mei

Man

Nguyen

. Fuzzy modeling and tracking control of nonlinear systems. Math Comput Modell 2001; 33: 759–770.

10.

Zheng

Duan

. Fuzzy learning tracking of a parallel cable manipulator. Mechatronics 2005; 15: 731–746.

11.

Parrilla

Riera

Torregrosa

. Fuzzy control for obstacle detection in object tracking. Math Comput Modell 2010; 52: 1228–1236.

12.

OpenCV library 2.0, http://sourceforge.net/projects/opencvlibrary/files/openvc-win/2.0/ (accessed May 2010).

13.

Malis

. Improving vision-based control using efficient second-order minimization techniques. In: IEEE international conference on robotics and automation, New Orleans, LA, April 2004, pp.1843–1848. Piscataway NJ: IEEE Press.

14.

Mudi

Pal

. A robust self-tuning scheme for PI-and PD-type fuzzy controller. IEEE Trans Fuzzy Syst 1999; 7: 2–16.

15.

Jung

Ham

Lee

. A real-time self-tuning fuzzy controller through scaling factor adjustment for the steam generator of NPP. Fuzzy Sets Syst 1995; 74: 53–60.

16.

Daniilidis

Krauss

Hansen

. Real-time tracking of moving objects with an active camera. Real-Time Imaging 1998; 4: 3–20.

17.

Piepmeier

McMuray

Lipkin

. Tracking moving target with model independent visual servoing: a predictive estimate approach. In: IEEE international conference on intelligent robot and system, Leuven, Belgium, 1998, pp.2652–2657. Piscataway NJ: IEEE Press.

18.

Welch

Bishop

. An introduction to the Kalman filter. Report, Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3175, July 2006.

19.

Kandel

Luo

Zhang

. Stability analysis of fuzzy control systems. Fuzzy Sets Syst 1999; 105; 33–48.

20.

Hans

Silvio

. A fuzzy approach to stability of fuzzy controllers. Fuzzy Sets Syst 1998; 96: 161–172.

21.

Lin

. Fuzzy stability analysis using interlacing condition. In: The 10th IEEE international conference on fuzzy systems, Melbourne, Australia, 2001, pp. 634–637. Piscataway NJ: IEEE Press.