| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥330,000)
Fiscal Year 2007: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2006: ¥2,600,000 (Direct Cost: ¥2,600,000)
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| Research Abstract |
A) In this research, we propose a K-means clustering based target tracking algorithm (called as K-means tracker), which can work robustly when tracking an object with hole through which the background can be seen (e.g. mosquito coil). The contributions of this research include: 1) Using a 5D feature vector to represent both the color "Y,U,V" and geometric "x,y" features of each pixel uniformly. This enables the simultaneous adaptation to both the color and geometric features during tracking; 2) Using a variable ellipse model to describe the search area and model the surrounding background. This guarantees the stable object tracking under various geometric transformations; 3) Achieving the automatic self tracking failure detection and recovery with both the "target" and "background" samples. Moreover, we propose a reliability-based K-means clustering algorithm (called as RK-means tracker) to remove the noise background pixel (which is neither similar to the target nor the background sam
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ples) from the target object. According to the triangular relationship among an unknown pixel and its two nearest cluster centers (target and background), the normal pixel (target or background one) will be assigned with high reliability value and correctly classified, while noise pixels will be given low reliability value and ignored. A radial sampling method is also brought out for improving both the processing speed and the robustness of this algorithm.
B) In this research, we propose a high-performance object tracking system for obtaining high-quality images of a high-speed moving object at video rate by controlling a Fixed Viewpoint Pan Tilt Camera (FV-PTZ Camera) . To achieve our goal, we use the K-means tracker algorithm for tracking objects in an image sequence captured by the active cameras. We use the results of the K-means tracker to control the angular position and speed of each pan-tilt-zoom unit by employing the PID control scheme. Moreover, by using two cameras, the binocular stereo vision algorithm can be used to obtain the 3D position and velocity of the object. These results are used in order to adjust the focus and zoom. Our system allows the two cameras to gaze at a single point in 3D space. However, this system may become unstable when the time response deteriorates by excessively interfering in a mutual control loop or by strict restriction of the camera action. In order to solve these problems, we introduce the concept of reliability into the K-means tracker, and propose a method for controlling the active cameras by using relative reliability. Less
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