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Automatic vehicle detection systems in urban and inter-urban traffic using computer vision are frequently based on background subtraction methods. Moving shadows represent a serious difficulty for these methods, as they will appear as part of the segmented foreground vehicles. Shadow removal algorithms usually rely on exploiting color properties. However,(More)
This paper proposes an enhanced version of the sigma delta background estimation method, suitable for urban traffic scenes. In the original algorithm, the background model quickly degrades in such complex scenes, being easily contaminated by slow moving or temporarily stopped vehicles. Some heuristics have been added to the basic algorithm in order to make(More)
Moving vehicle detection is an essential process for Intelligent Transportation system. During the last decade, a large amount of work has been trying to produced output for this challenge; however, performances of most of them still fall far behind human perception. In this paper the object detection problem is studied, analyzing and reviewing the most(More)
We show how asymmetries in the movement patterns during the process of regaining balance after perturbation from quiet stance can be modeled by a set of coupled vector fields for the derivative with respect to time of the angles between the resultant ground reaction forces and the vertical in the anteroposterior and mediolateral directions. In our model,(More)
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