一种大气湍流环境下的运动目标自适应检测方法

Image sequences acquired from remote distance remote distance are easily influenced by atmospheric turbulence, and subject to random changes of intensity, twinkle of pixels and shifts of object positions. Traditional background modeling methods are not able to detect the object of interest correctly in the turbulent environment. In this paper, a decision-making approach with multiple hierarchies is proposed to model the effect of atmospheric turbulence. Gaussian and double Gaussian distributions are utilized to model and distinguish the pixels in the flat and edge regions of the background, respectively. All parameters are updated online. Moreover, adaptive threshold is employed to discriminate between abrupt changing pixels and pixels in the object region by fusing the results of the first level. Finally, the region of the moving object is obtained by the connected component constraint. Comparison with other methods shows that the method performs well under different situations, such as different turbulence strength variations, different numbers of objects, and different moving directions.