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Dimitrijevic, M. (2006) Detecting Human Body Posture Using Spatial-Temporal Bayesian Models. International Conference on Intelligent and Advanced Systems, 10, 127-139.

has been cited by the following article:

  • TITLE: Tracking a Moving Objects Using Foreground Detector and Improved Morphological Filter

    AUTHORS: Diallo Mamadou Saidou

    KEYWORDS: Tracking, Background, Foreground, Gaussian Mixture Model, Morphological Filter

    JOURNAL NAME: Open Access Library Journal, Vol.4 No.12, December 22, 2017

    ABSTRACT: 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 increases.