This paper proposed a algorithm for detecting moving object based on camera relation motion. First, the rotational parameter models are built for global motion images. Secondly, SIFT point pairs are established based on motion prediction between successive frames and RANSAC algorithm is used to eliminate the outliers. With the least square method precisely computing the motion compensation, an updating strategy based on residual image, which updates the feature point set sequentially, can adjust the change of the background. Finally, motion object is detected by Frame difference method. This novel algorithm remains the advantages of SIFT and raises the detection rate significantly. The experiment results demonstrate that this algorithm can effectively detect objects while achieving real-time performance.%论文提出了一种摄像机旋转运动下的快速目标检测算法.首先为图像的全局运动建立旋转参数模型,然后基于运动预测在相邻帧之间建立SIFT特征点对,利用RANSAC去除外点的影响,结合最小二乘法求解全局运动参数进行运动补偿,基于残差图像的更新策略实时更新特征点集,以适应背景的变化,最后使用帧差法获得运动目标.该算法不仅保持了SIFT本身的优越性能,而且极大地提高了检测速度.实验结果表明该算法可以实时准确的检测出运动目标.
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