An Adaptive Method for Moving Object Detection in Atmospheric Turbulence Environment
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摘要: 在远距离成像过程中，图像序列受到湍流的影响会出现像素点亮度的随机起伏、闪烁和图像中物体的位置漂移，这使得传统的背景建模方法在湍流环境下难以准确检测运动目标.针对图像受到湍流影响在不同区域表现出的不同性质，提出分层次决策判别方法.首先，用高斯模型建模背景平坦区域，用双高斯模型建模背景中的物体边缘区域，设定判别式对每个像素点进行判别，并在线更新模型参数；然后，针对由于湍流影响出现的亮度突变点建立自适应判别模型，结合前一层次的判别结果，构造判别条件消除亮度突变点，分割得到目标点；最后，通过连通区域约束得到目标区域.实验结果表明，本文方法在不同湍流强度下对不同数量和不同运动方向的目标取得了良好的检测效果.Abstract: 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.1) 本文责任编委 赖剑煌
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图 1 大气湍流中分层次运动目标检测框架
Fig. 1 Hierarchical framework of moving object detection in atmospheric turbulence environment
图 2 不同大气湍流强度的图像序列中不同像素点随时间变化的亮度变化曲线图和分布直方图
Fig. 2 The intensity curves and histograms of points in image sequences affected by atmospheric turbulence with different strength
图 4 两个不同大气湍流强度的图像序列得到的$Offset$变化曲线
Fig. 4 The curves of $Offset$ values in two image sequences affected by atmospheric turbulence with different strength
图 5 平坦区域一点和有目标经过位置一点的阈值随时间的变化
Fig. 5 The threshold curves of two points in flat regions and regions with objects passing through
图 6 各个评价值随边缘区域窗口尺寸变化的曲线
Fig. 6 The curves of criteria varying over window sizes of the edge region
图 7 各个评价值随式(10)中比例因子$\lambda$变化的曲线
Fig. 7 The curves of criteria varying over $\lambda$
图 8 各个评价值随像素点邻域窗口尺寸变化的曲线
Fig. 8 The curves of criteria varying over window sizes of the neighborhood
序号 分辨率 帧频(Hz) 帧数 大气湍流强弱/信噪比(dB) 图像序列描述 序列1 $720 \times 576$ 30 425 弱/26.70 有一个向右缓慢移动的目标, 目标较小, 速度较慢. 序列2 $672 \times 480$ 30 397 弱/25.59 有多个左右缓慢移动的目标和多个左右快速移动的目标. 序列3 $672 \times 480$ 30 390 弱/28.08 有多个左右缓慢移动的目标和一个向左快速移动的目标. 序列4 $672 \times 480$ 30 401 强/19.59 有多个向右快速移动的目标. 序列5 $672 \times 480$ 30 400 强/15.31 只有少部分图像帧存在向右快速移动的目标. 序列6 $672 \times 480$ 30 493 强/16.66 只有第120帧前有向右快速移动的目标. 
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表 2 序列1中背景模型更新与否得到的评价值对比
Table 2 Comparison between updating models and not updating models for sequence one
Precision Recall $F1$ $SR$ 更新模型 0.89 0.95 0.92 0.97 不更新模型 0.47 0.95 0.63 0.73
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表 3 各方法在6个图像序列中检测目标得到的Precision/Recall值比较
Table 3 Precision and Recall results of different methods
方法 序列1 序列2 序列3 序列4 序列5 序列6 本文方法 0.89/0.95 0.97/0.85 0.87/0.97 0.92/0.98 0.62/0.56 0.83/0.95 Chen等[19] 0.93/0.36 0.90/0.50 0.98/0.20 0.95/0.63 0.77/0.38 0.87/0.89 Oreifej等[17] 0.56/0.60 0.85/0.57 0.92/0.80 0.20/0.52 0.00/0.00 0.00/0.00 Stauffer等[3] 0.00/0.00 0.90/0.46 0.96/0.48 0.25/0.68 0.06/0.81 0.03/0.59 Barnich等[8] (阈值40) 0.19/0.48 0.71/0.86 0.63/0.67 0.19/0.83 0.02/0.44 0.02/0.73 Barnic等[8] (阈值80) 0.00/0.00 0.81/0.47 1.00/0.03 0.53/0.82 0.03/0.58 0.06/0.76 Noh等[13] 0.00/0.00 0.98/0.67 1.00/0.19 0.67/0.56 0.00/0.02 0.21/0.73 St-Charles等[14] (SuBSENSE) 0.76/0.93 0.90/0.90 0.96/0.64 0.35/0.75 0.05/0.63 0.04/0.84 St-Charles等[15] (PAWCS) 0.40/0.99 0.86/0.87 0.85/0.97 0.18/0.69 0.02/0.40 0.02/0.70
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表 4 各方法在6个图像序列中检测目标得到的$F1$值比较
Table 4 The $F1$ results of different methods
方法 序列1 序列2 序列3 序列4 序列5 序列6 本文方法 0.92 0.91 0.92 0.95 0.59 0.89 Chen等[19] 0.52 0.64 0.33 0.76 0.51 0.88 Oreifej等[17] 0.58 0.68 0.86 0.29 0.00 0.00 Stauffer等[3] 0.00 0.61 0.64 0.36 0.12 0.05 Barnich等[8] (阈值40) 0.28 0.78 0.65 0.31 0.03 0.04 Barnich等[8] (阈值80) 0.00 0.59 0.05 0.65 0.06 0.11 Noh等[13] 0.00 0.80 0.32 0.61 0.01 0.32 St-Charles等[14] (SuBSENSE) 0.84 0.90 0.77 0.47 0.09 0.08 St-Charles等[15] (PAWCS) 0.57 0.86 0.91 0.29 0.03 0.04
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表 5 各方法在6个图像序列中检测目标得到的$SR$值比较
Table 5 The $SR$ results of different methods
方法 序列1 序列2 序列3 序列4 序列5 序列6 本文方法 0.97 0.64 0.76 0.85 0.84 0.98 Chen等[19] 0.72 0.18 0.06 0.43 0.84 0.99 Oreifej等[17] 0.78 0.22 0.64 0.01 0.04 0.00 Stauffer等[3] 0.55 0.15 0.33 0.05 0.14 0.27 Barnich等[8] (阈值40) 0.75 0.41 0.24 0.00 0.00 0.00 Barnich等[8] (阈值80) 0.59 0.08 0.02 0.12 0.00 0.05 Noh等[13] 0.59 0.20 0.12 0.27 0.50 0.83 St-Charles等[14] (SuBSENSE) 0.93 0.72 0.50 0.16 0.19 0.24 St-Charles等[15] (PAWCS) 0.76 0.63 0.74 0.02 0.00 0.18
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