This paper presents object detection and tracking algorithm which can adapt to object color shift. In this algorithm, we train and build multi target models using color under different illumination conditions. Each model called as Color Distinctiveness look up Tables or CDT. The color distinctiveness is the value integrating 1) similarity with target colors and 2) dissimilarity with non-target colors, which represents how distinctively the color can be classified into target pixel. Color distinctiveness can be used for pixel-wise target detection, because it takes 0.5 for colors on decision boundary of nearest neighbor classifier in color space. Also, it can be used for target tracking by continuously finding the most distinctive region. By selecting the most suitable CDT for camera direction, lighting condition, and camera parameters, the system can adapt target and background color change. We implemented this algorithm for a Pan-tilt stereo camera system. Through experiments using this system, we confirmed that this algorithm is robust against color shift caused by illumination change and it can measure the target 3D position at video rate.
In this paper, 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 pair of active cameras mounted on two fixed view point pan-tilt-zoom units. In this paper, the 'High quality object image' means that the image of the object is in focus and not blurred, the S/N ratio is high enough, the size of the object in the image is kept unchanged, and the position the object appearing at the image center. To achieve our goal, we use K-means tracker algorithm for tracking object in image sequence which taken by the active cameras. We use the result of the K-means tracker to control the angular position and speed of each pan-tilt-zoom unit by employing PID control scheme. By using two cameras, binocular stereo vision algorithm can be used to obtain 3D position and velocity of the object. These results are used for adjust the focus and the zoom. Moreover, our system let two cameras fix its eyes on one point in 3D space. However, this system may be unstable, when time response loses by interfering in a mutual control loop too much, or by hard restriction of cameras action. In order to solve these problems, we introduced a concept of reliability into K-means tracker, and propose a method for controlling active cameras by using relative reliability. We produce the prototype system. Though extensive experiments we confirmed that we can obtain in focus and motion-blur-free images of a high-speed moving object at video rate.
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