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On line background modeling for moving object segmentation in dynamic scenes

机译:在线背景建模,用于动态场景中的运动对象分割

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摘要

Fast and accurate moving object segmentation in dynamic scenes is the first step in many computer vision applications. In this paper, we propose a new background modeling method for moving object segmentation based on dynamic matrix and spatio-temporal analyses of scenes. Our method copes with some challenges related to this field. A new algorithm is proposed to detect and remove cast shadow. A comparative study by quantitative evaluations shows that the proposed approach can detect foreground robustly and accurately from videos recorded by a static camera and which include several constraints. A Highway Control and Management System called RoadGuard is proposed to show the robustness of our method. In fact, our system has the ability to control highway by detecting strange events that can happen like vehicles suddenly stopped in roads, parked vehicles in emergency zones or even illegal conduct such as going out from the road. Moreover, RoadGuard is capable of managing highways by saving information about the date and time of overloaded roads.
机译:动态场景中快速,准确的运动对象分割是许多计算机视觉应用程序的第一步。本文提出了一种基于动态矩阵和场景时空分析的运动物体分割背景建模新方法。我们的方法可以应对与此领域相关的一些挑战。提出了一种检测和消除投影阴影的新算法。通过定量评估进行的比较研究表明,该方法可以从静态摄像机录制的视频中可靠且准确地检测到前景,并且该视频包含多个约束。提出了一种称为RoadGuard的公路控制和管理系统,以显示我们方法的鲁棒性。实际上,我们的系统具有通过检测可能发生的奇怪事件来控制高速公路的能力,这些异常事件可能会发生,例如突然停在道路上的车辆,在紧急情况下停放的车辆,甚至是违法行为,例如从道路上出来。此外,RoadGuard能够通过保存有关超载道路的日期和时间的信息来管理高速公路。

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