航拍图像序列的地面运动目标跟踪

建立了一套针对航拍图像的运动目标跟踪系统。根据航拍图像序列存在背景运动的特点，运用了一种基于稀疏采样的局部补偿误差函数泰勒展开法，求得背景模型参数，补偿背景运动；根据背景运动矢量和目标运动矢量不同的特点，采用了运动矢量聚类的算法，对多个潜在目标准确定位，并建立跟踪嚣找出真实目标进行跟踪。针对图像匹配可能产生的误差。提出对匹配结果进行聚类，以提高求得的背景模型参数的精确性，又针对差分图像中运动目标信息的不完整的特点，根据目标残缺信息先取得目标完整边缘信息，再进行目标定位，提高了定位的精度，并给出了部分实验结果。结果表明，该算法对噪声和光照具有很强的抑制性，能快速有效地对运动目标进行跟踪。

Tracking moving objects in video from an airborne observer

In order to detect and track moving targets in aerial video, a tracking system was established. For the characteristic of background movement of the aerial video, a sparse sample-based method containing Taylor expansion of local compensation error function was used to estimate the background movement model. A motion-vector clustering algorithm was presented to locate the potential targets in the video, relevant trackers were then constructed to select true moving targets and perform long-term tracking of them. In order to improve the precision of the background movement model parameter, the result of image matching was clustered. Using the fragmentary information of the moving targets to gain the targets edge, the precision of locating the targets was improved. The results show that the method is effective even in environment with heavy noise and light-effects.