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Identifying moving objects in a video using modified background subtraction and optical flow method

机译:使用改进的背景减法和光流方法识别视频中的运动对象

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To identify the object in a video that changes its position from time to time in a video is amongst the crucial steps in various application of computer vision like extraction of objects, video surveillance, classification and pattern identification as well. In this paper a novel approach is proposed that is used to detect the objects that are not stationary in noisy environment. In the proposed algorithm Gaussian mixture model is used to extract the moving object and then the Optical flow method is used to acquire error free results quickly followed by frame differencing to obtain accurate extraction of shapes. The algorithm has been analyzed and tested over various videos in different environmental circumstances. The result acquired proves the algorithm to be more reliable, the detection is more precise and even low in terms of cost and memory requirement as well.
机译:识别视频中不断变化的视频中的对象是计算机视觉在各种应用中的关键步骤,例如提取对象,视频监视,分类和模式识别。在本文中,提出了一种新颖的方法,该方法用于检测在嘈杂环境中不平稳的对象。在提出的算法中,使用高斯混合模型提取运动对象,然后使用光流方法快速获取无差错结果,然后进行帧差分以准确提取形状。该算法已在不同环境条件下的各种视频上进行了分析和测试。获得的结果证明该算法更可靠,检测更精确,甚至在成本和存储需求方面甚至更低。

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