Abstract In order to improve the computational speed and detection accuracy of the ViBe algorithm in foreground edge detection, an improved algorithm based on ViBe moving objects edge detection is proposed in this study by using the partial neighborhood model H(x, y) of a pixel at the point (x, y) to find the absolute difference between H(x, y) and the pixel’s original background model M(x, y) to determine if the pixel is moving. According to the nature of the algorithm, a method based on offset thread block coordinates to optimize thread divergence is proposed from the perspective of calculation level of kernel function and a CUDA (computing unified device architecture) based method is propsed to optimize the stream transmission between CPU and GPU in order to the run time efficiency of the new algorithm. The experimental results indicated the improved algorithm implements ghost elimination, avoids large-area irrelevant background edges and achieves better efficiency and accuracy.

中文翻译：

---

一种基于ViBe的运动目标边缘检测算法及其并行实现

摘要 为了提高ViBe算法在前景边缘检测中的计算速度和检测精度，本研究利用像素的部分邻域模型H(x,y)提出一种基于ViBe运动物体边缘检测的改进算法。在点 (x, y) 处找到 H(x, y) 与像素的原始背景模型 M(x, y) 之间的绝对差异，以确定像素是否在移动。根据算法的性质，从核函数计算层次的角度提出了一种基于偏移线程块坐标优化线程发散度的方法，并提出了一种基于CUDA（计算统一设备架构）的方法来优化线程间的流传输。 CPU和GPU为了新算法的运行时间效率。