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Object tracking is a challenging problem due to the presence of noise, occlusion, clutter and dynamic change in the scene other than the motion of object of interest. A variety of tracking algorithms has been proposed and implemented to overcome these difficulties, but there are still some problems need to be covered. This paper proposed an algorithm to track the moving objects employing a color-based...
We proposed a method for automatic detection and tracking of moving object employing a particle filter in conjunction with a color feature method. The particle filtering is used because it is robust for non-linear and non-Gaussian dynamic state estimation problems and performs well when clutter and occlusions are present on the image. A histogram-based framework is used to describe the color feature...
In this paper, we propose a method for identification of persons using feature points which are taken from different angle and estimating of face direction using eigenspace technique. The face images are extracted on image sequences which are captured by a camera using image processing techniques. The face directions are estimated based on the eigenspace technique. Furthermore, the locations of face...
In this paper, we propose a new technique to detect and track the interested moving person employing a block matching technique. The blocks are defined by dividing the image frame into non-overlapping square parts and made in the previous and current frame. The blocks are made based on the peripheral increment sign correlation image. The increment sign of each block is calculated by comparing the...
Automatic detection and tracking of moving object is very important task for security system, monitoring activity and surveillance application. It is, however, still under the developmental stage and needs to be robust performance when applied in an unconstrained environment. Many approaches have been developed to detect motion, namely, optical flow, frame difference, background subtraction and skin...
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