Background subtraction, being the most cited algorithm for foreground detection, encounters the major problem of proper threshold value at run time. For effective value of the threshold at run time in background subtraction algorithm, the primary component of the foreground detection process, motion is used, in the proposed algorithm. For the said purpose, the smooth histogram peaks and valley of the motion were analyzed, which reflects the high and slow motion areas of the moving object(s) in the given frame and generates the threshold value at run time by exploiting the values of peaks and valley. This proposed algorithm was tested using four recommended video sequences, including indoor and outdoor shoots, and were compared with five high ranked algorithms. Based on the values of standard performance measures, the proposed algorithm achieved an average of more than 12.30% higher accuracy results.

中文翻译：

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在高分辨率大数据集中使用运动直方图阈值算法进行前景检测

背景减法作为前景检测中引用最多的算法，在运行时遇到了正确阈值的主要问题。对于背景减法算法运行时阈值的有效值，在所提出的算法中使用了前景检测过程的主要组件运动。为此，对运动的平滑直方图波峰和波谷进行了分析，它反映了给定帧中运动物体的高低运动区域，并在运行时利用峰的值生成阈值和山谷。该算法使用四个推荐的视频序列进行了测试，包括室内和室外拍摄，并与五个排名靠前的算法进行了比较。根据标准绩效指标的值，