Mobile object detection is
one of the most important steps in computer vision applications such as: medical
analysis human-machine interface, robotics, traffic monitoring, and more. In
this article, we apply the Gaussian mixing model which is established on the
background subtraction. A smoothing method was used for the pre-processing step
and a morphological filter was applied to remove unwanted pixels from the
background in the other to solve the problem of background noise disturbance.
We also demonstrated that filtering foreground segmentation twice with the same
morphological structured element but with a different width was used to improve
the accuracy of the result. The results show that the proposed method is
effective in detecting and tracking moving vehicles, compared to filter
segmentation in the foreground only once. Several methods and algorithms have
been used to solve this problem. All the methods used before have been
effective but also have limits. Some of these methods lose the object when the
number of frames is wide while others lose it when it changes direction or
rolls at a high speed. In addition, the algorithms proposed for the detection
of colors in RGB also lose their objectives when the object changes the color.
But the proposed combination in this paper maintains contact with the object
without losing it even if it changes direction or speed or the number of frame
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