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Sensor Planning in Smart Vision Systems |
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| Aly A. Farag | CVIP Director | farag@cvip.uofl.edu | ||
| Alaa El-din A. Aly | Research Assistant | alaa@cvip.uofl.edu | ||
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We have developed a sensor planning approach for a mobile trinocular active vision system [1-2]. At the stationary state (i.e., no motion) the sensor planning system calculates the generalized cameras' parameters (i.e., translational distance from the center, zoom, focus and vergence) using deterministic geometric specifications of both the sensors and the objects in their field of view. Some of these geometric parameters are difficult to be predetermined for the mobile system operation. Therefore, we have developed a new sensor planning approach based on processing the content of the captured images. The approach uses a combination of a closed-form solution for the translation between the three cameras, the vergence angle of the cameras as well as zoom and focus setting with the results of the correspondences between the acquired images and a predefined target object(s) obtained using the SIFT algorithm The work we have developed in [1] was restricted to the design of the CardEye. As a generalization, we have presented a novel and robust model for camera planning in any smart vision systems [3]. This approach uses virtual forces to adjust camera parameters (pan and tilt) to the most proper values with respect to the application. The proposed model employs the information in the acquired image and some of the intrinsic camera parameters to estimate pan and tilt displacements required to bring a target object into a specific location of interest in the image. This model is a general framework and any vision system can be easily modeled to use it. This approach has several advantages over previous work in camera planning. It is portable, expandable, robust, and flexible. Also, there is no need for complicated calibration for the cameras or their pan-tilt heads. The results show that our approach is efficient even with poor system initialization and it is robust against possible weakness in the auxiliary algorithms used. |
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Before Planning After Planning |
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[1] Aly Farag and Alaa E. Abdel-Hakim, "Image Content-Based Active Sensor Planning for a Mobile Trinocular Active Vision System,” Proc. IEEE International Conference on Image Processing (ICIP’2004), Singapore, October 2004, Vol. II, pp. 193-196. |
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[2] Aly Farag and Alaa E. Abdel-Hakim, " Scale Invariant Features for Camera Planning in a Mobile Trinocular Active Vision System,” Proc. Advanced Concepts in Intelligent Vision Systems (ACIVS’2004), Brussel, Belgium, August-September 2004, pp. 169-176. |
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[3] Aly A. Farag and Alaa E. Abdel-Hakim, “Virtual Forces for Camera Planning in Smart Vision Systems,” Proceedings IEEE Workshop on Applications of Computer Vision (WACV 2005), Breckenridge CO, January 2005. |
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We would like to thank the US army for its sponsorship.
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