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A new approach for real-time segmenting moving objects under cluttered background

机译:杂乱背景下实时分割移动物体的一种新方法

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Concerning traditional motion segmenting algorithms, such as TD and GMM, can not meet the actual requirement that not only be robust stability to minor disturbance but also response rapidly while facing abrupt background changes, we propose a novel method named IDGMM to solve this problem by modeling the Inter-frame Differencing image with Gaussian Mixture Models. By taking both the long-term statistical analysis and instantaneous change response into account, this method is able to gain better performances than traditional algorithms. Experimental results on standard video sequences extremely show the effectiveness and robustness of this method.
机译:关于传统运动分段算法,如TD和GMM,不能满足实际要求,这不仅是对轻微干扰的稳健稳定性,而且在面对突然背景的情况下迅速响应,我们提出了一种名为IDGMM的新方法来解决这个问题来解决这个问题高斯混合模型的帧间差分图像。通过考虑到长期统计分析和瞬时变化响应,这种方法能够比传统算法获得更好的表现。标准视频序列的实验结果极大地显示了这种方法的有效性和鲁棒性。

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