Abstract
The detection of moving objects in a video sequence is an essential step in almost all the systems of vision by computer. However, because of the dynamic change in natural scenes, the detection of movement becomes a more difficult task. In this work, we propose a new method for the detection moving objects that is robust to shadows, noise and illumination changes. For this purpose, the detection phase of the proposed method is an adaptation of the MOG approach where the foreground is extracted by considering the HSV color space. To allow the method not to take shadows into consideration during the detection process, we developed a new shade removal technique based on a dynamic thresholding of detected pixels of the foreground. The calculation model of the threshold is established by two statistical analysis tools that take into account the degree of the shadow in the scene and the robustness to noise. Experiments undertaken on a set of video sequences showed that the method put forward provides better results compared to existing methods that are limited to using static thresholds.
Highlights
The detection of moving objects in a video sequence is an important task in computer vision
Model of the Background Several color models such as RGB, HSV, YUV and L*a*b spaces have been used for the statistical modeling of the background of a video sequence, In our case, we propose to use the HSV color space whose advantage lies in its invariance regarding luminosity
The results show that our method based on dynamic thresholding using the HSV color space provides better results both when detecting objects and removing shadows
Summary
The detection of moving objects in a video sequence is an important task in computer vision. Note that the step of detecting can be very complex due to the presence of disruptive elements in the environment of the object. Factors such as weather conditions, changes in lighting of the scene, the presence of shadows or moving objects in the scene (movement of branches of a tree, window movement, a computer screen, etc ...) may negatively influence the detection process. To make up for these problems, several approaches have been proposed in the literature. These include the background of modeling methods that can be classified into several categories: basic, statistical, vague, etc... A thorough analysis of these methods has demonstrated that these statistical methods are generally more robust to illumination changes and dynamic background [17], [10]
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