基于熵和相关接近度的混合高斯目标检测算法

针对固定模型个数的混合高斯模型的背景建模速度慢和运动目标的拖影问题,提出了一种基于Tsallis熵和相关接近度的改进混合高斯算法。该算法利用Tsallis熵对高斯模型自适应地选择模型个数,加速背景建模；对于模型匹配判断条件,不能很好地体现相邻像素点的空间相关性的情况,提出了相关接近度作为模型更新的限定条件,以去除拖影。实验结果表明,改进的算法在实时性、检测正确率方面都有较好的改进。

Mixed Gaussian Target Detection Algorithm Based on Entropy and Related Close Degree

Aiming at that the background modeling of the hybrid gaussian model with fixed model number is slow and the detected moving targets have following contour when they move,an imprvoed moving object detection method based on mixture gaussian model with Tsallis entropy and related close degree was proposed.The improved algorithm automatically chooses model numbers to accelerate the background modeling.For model matching judgment condition cannot reflect spatial correlation of adjacent pixels, this paper proposed the conception of related close degree as another qualification condition to remove following contour.The experimental results show the improved algorithm greatly improves in real-time and detection accuracy.