Study on an environmental embedding type "eye camera"
| Project/Area Number |
16500112
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Wakayama University |
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Principal Investigator |
WU Haiyuan Wakayama University, Faculty of Systems Engineering, Associate Professor, システム工学部, 助教授 (70283695)
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| Co-Investigator(Kenkyū-buntansha) |
WADA Toshikazu Wakayama University, Faculty of Systems Engineering, Professor, システム工学部, 教授 (00231035)
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| Project Period (FY) |
2004 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥3,700,000 (Direct Cost: ¥3,700,000)
Fiscal Year 2005: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2004: ¥2,900,000 (Direct Cost: ¥2,900,000)
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| Keywords | Visual direction estimation / Two-circle method / Eve model / Tracking the contour of the iris / Case / instance based learning / Relative reliability / Active camera control / Stereo vision / 黒目輪郭の追跡 |
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| Research Abstract |
In this research, we propose a sophisticated method to estimate visual direction using iris contours. This method requires only one monocular image taken by a camera with unknown focal length. In order to estimate the visual direction, we assume the visual directions of both eyes are parallel and iris boundaries are circles in 3D space. In this case, the two planes where the iris boundaries reside are also parallel. We estimate the normal vector of the two planes from the iris contours extracted from an input image by using an extended "two-circle" algorithm.
In this research, we also propose a general object tracking method (called as "K-means tracker"), which is realized by classifying the pixels within the search area into "target" and "background" groups with a K-means clustering based algorithm. We first use a 5D feature vector to describe both the color ("Y,U,V") and the position ("x,y") of each pixel uniformly. This enables the simultaneous adaptation to both the color and geometric features during tracking. Secondly, we use a variable ellipse model to describe the shape of the search area and to model the surrounding background. This guarantees the stable object tracking under various geometric transformations.
Moreover, we propose a high performance object tracking system for obtaining high quality images of a moving object at video rate by controlling an active camera mounted on a motor droved pan-tilt unit. We use "K-means tracker" for tracking object on image space. The PID control scheme is employed for controlling the angular position and speed of the pan-tilt units according to the results of the "K-means tracker". With this system, we can obtain motion-blur-free images of a moving object.
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Report
(3 results)
Research Products
(38 results)
