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Efficient Moving Object Detection for Lightweight Applications on Smart Cameras

机译:轻巧应用在智能相机上的高效运动物体检测

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摘要

Recently, the number of electronic devices with smart cameras has grown enormously. These devices require new, fast, and efficient computer vision applications that include moving object detection strategies. In this paper, a novel and high-quality strategy for real-time moving object detection by nonparametric modeling is presented. It is suitable for its application to smart cameras operating in real time in a large variety of scenarios. While the background is modeled using an innovative combination of chromaticity and gradients, reducing the influence of shadows and reflected light in the detections, the foreground model combines this information and spatial information. The application of a particle filter allows to update the spatial information and provides a priori knowledge about the areas to analyze in the following images, enabling an important reduction in the computational requirements and improving the segmentation results. The quality of the results and the achieved computational efficiency show the suitability of the proposed strategy to enable new applications and opportunities in last generation of electronic devices.
机译:近来,带有智能相机的电子设备的数量已大大增加。这些设备需要新的,快速而高效的计算机视觉应用程序,其中包括移动物体检测策略。本文提出了一种新颖的高质量非参数建模实时运动目标检测策略。它适用于在各种情况下实时运行的智能相机。使用创新的色度和渐变组合对背景进行建模,可以减少阴影和反射光在检测中的影响,而前景模型则将这些信息与空间信息结合在一起。粒子过滤器的应用允许更新空间信息,并提供有关要在后续图像中分析的区域的先验知识,从而可以大大减少计算需求并改善分割结果。结果的质量和所获得的计算效率表明了所提出的策略的适用性,以在上一代电子设备中实现新的应用和机会。

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