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Detecting a high-quality moving object with good robustness in computer vision system has important significance for follow-up task. Researching on the traditional algorithm, this paper proposes a background reconstruction algorithm based on a modified k-means clustering and the Single Gaussian model which could provide an accurate background image through a sequence of scene images with foreground...
An improved monocular vision method is studied for intelligent vehicle to detect the preceding car in the structural road environment. Through identifying the edges of the car, the object is detected; the false object is eliminated and the eligible object expressed as a 2-D model is acquired. Then the location of object in the next frame is predicted by Kalman filter, and the object is detected near...
For improving accuracy and robust property of human detection, fusion of image sequences captured from visible-thermal sensors is lucrative. Instead of performing it in pixel-level directly, we try to fuse object features by a novel image sequence fusion algorithm based on gradient feature (GFIF). The GFIF algorithm calculate gradients of input images to form a joint histograms of Oriented Gradient...
Total robustness to illumination change is difficult to solve just using a single approach. Our approach to improvement in accuracy and robustness to illumination change is by fusing two different methods, namely, color constancy and color co-occurrence. A “grey world” assumption is used to handle sudden illumination change while color cooccurrence modelling is used to handle gradual illumination...
Usually, the video based object tracking deal with non-stationary image stream that changes over time. Robust and Real time moving object tracking is a problematic issue in computer vision research area. Multiple object tracking has many practical applications in scene analysis for automated surveillance. If we can track a particularly selected object in an environment of multiple moving objects,...
Multiple-extremum issue including the well-known ??singularity?? problem is one of the major defects in kernel-based object tracking. This paper studies this important problem and presents a novel approach called section-based tracking (SBT) that is based on the section information provided by the division of the object's weight image. This approach serves to eliminate fake extremal points and make...
Background subtraction algorithms are critical to many video recognition/analysis systems and have been studied for decades. Most of the algorithms assume that the camera is fixed. We propose a background subtraction algorithm that works when a shaking camera is present. In this algorithm, the input frames are compensated and compared with the given reference frame to separate foreground objects from...
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