ABSTRACT Background subtraction is generally used to detect moving objects, because it has low complexity, and it is easy to implement. However, the detection error increases when the background is changing. Therefore, adaptive background subtraction is applied to overcome this problem, and it continuously requires updating the background with a fixed learning rate. The learning rate should be tuned for a consistently evolving background. This paper proposes the Probabilistic Static Foreground Elimination for Background Subtraction (PSFE) algorithm. It consisted of two parameters: the number of frames for static foreground elimination, and the probability of changes in background pixels. These two parameters can tune the learning rate and update background for better detection. The average results of the detection error rate from Wallflower datasets were tested with PSFE and well-known method. They demonstrated that PSFE provides moving object detection with minimum detection error (5.95%), especially in Camouflage, Moved object, and Light switch dataset.

中文翻译：

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背景减法的概率静态前景消除

摘要背景减法由于复杂度低、易于实现，一般用于检测运动物体。但是，当背景发生变化时，检测误差会增加。因此，应用自适应背景减法来克服这个问题，它需要以固定的学习率不断更新背景。学习率应该针对不断变化的背景进行调整。本文提出了用于背景减法的概率静态前景消除（PSFE）算法。它由两个参数组成：静态前景消除的帧数和背景像素变化的概率。这两个参数可以调整学习率并更新背景以更好地检测。使用 PSFE 和众所周知的方法测试了 Wallflower 数据集检测错误率的平均结果。他们证明了 PSFE 提供了具有最小检测误差 (5.95%) 的运动物体检测，尤其是在伪装、运动物体和灯光开关数据集中。