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Detection and Segmentation of Moving Objects Based on Support Vector Machine

机译：基于支持向量机的运动目标检测与分割

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In order to improve the accuracy of multi-moving objects detection in surveillant video, this paper presents a new method of detection and segmentation for moving objects based on SVM (support vector machine). To further enhance the accuracy of segmentation using support vector machine, we modify the kernel function based on its nature, and some experiments have been done to compare with other kernel functions commonly used. The experimental results show that the classifier with the kernel function of RBF + Gaussian RBF has the better classification performance. We also compare our algorithm with frame difference and background subtraction method. Experiments show that our algorithm is effective and robust for the coming, gradient and leaving of moving objects in video, and it is immune to the illumination changes in scene and the speed changes of moving objects movement, besides, no significant non-connectivity exists in the detected moving objects. Moreover, no thresholds, which are often hard to select in most segmentation methods, are involved in our algorithm.
机译：为了提高监视视频中多目标检测的准确性，提出了一种基于支持向量机的运动目标检测与分割方法。为了进一步提高使用支持向量机进行分割的准确性，我们根据其性质修改了核函数，并进行了一些实验以与常用的其他核函数进行比较。实验结果表明，具有RBF +高斯RBF核函数的分类器具有更好的分类性能。我们还将我们的算法与帧差和背景减法进行了比较。实验表明，该算法对视频中运动物体的出现，倾斜和离开均有效且鲁棒，并且不受场景光照变化和运动物体运动速度变化的影响，此外在视频中没有明显的非连通性。检测到的移动物体。此外，在我们的算法中，没有阈值（通常在大多数分割方法中通常很难选择）。

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