针对室外视频监视中运动对象检测易受树枝叶晃动、水面波动等无意义运动干扰，准确性低、实时性差的问题，定义感兴趣区域为已经存在及潜在存在有意义运动对象的区域，提出一种感兴趣区域自动提取算法。构造带状算子提取训练阶段存在有意义运动对象的区域，利用颜色一致区域生长和干扰对象区域退化得到潜在存在有意义运动对象的区域，对不同区域采取不同的检测策略可以提高检测的准确性和实时性。实验结果表明，该算法对感兴趣区域提取结果良好，用于室外视频监视中运动对象的检测能克服无意义运动干扰，提高检测的准确性，并能有效减少计算量。

Motion objects detection in outdoor video surveillance is prone to be disturbed by insignificant motions, such as branches swing and wave, and has low accuracy and bad real-time. So a Region of Interest(ROI) automatic extraction algorithm is proposed in this paper, and ROI has the existing and potential significant motion objects. The algorithm constructs the belt-shaped operators to detect the region existing motion objects, and realizes region growing based on color similarity and region degeneration based on disturbance objects, and then gets the potential significant motion region. Adopting the different detection strategies for different regions can improve the accuracy in real-time. Experimental results show that the algorithm is efficient in extracting ROI. In motion objects detection application, the approach can overcome the influence of insignificant motions, improve the accuracy, and reduce the computation complexity greatly.