摘要
摄像机的运动使得复杂背景下动目标的检测复杂化。为了应对动态变化的背景,本文提出基于SIFT特征匹配和运动历史图的目标检测算法。首先用SIFT算法提取特征点,采用RANSAC方法求得仿射变换模型参数并实现图像的全局运动补偿,最后利用运动历史图的方法检测出动目标。SIFT特征点匹配的准确性和RANSAC方法去除异常点的有效性使得仿射变换模型参数计算准确,运动历史图则给出了动目标清晰的轮廓,并指明了动目标的运动方向。与Ninad Thakoor实验结果对比说明:该算法能够准确地检测出动目标,并且显示了动目标的运动方向。
The movement of a camera makes the moving objects detection more difficult under a complex background. In order to cope with the dynamically changed background, we propose an object detection method based on the SIFT features match and Motion History Image. Firstly, the feature points are detected by the SIFT algorithm to compute the parameters of the affine transform model, guided by RANSAC, to compensate the global motion between images. Secondly, we adopt the MHI method to detect moving objects. The robustness of the SIFT features match and the validity of picking out the outliers by the RANSAC algorithm make the parameters of the affine transform model compute accurately, and MHI shows the moving objects and the moving direction of objects clearly. The experimental results demonstrate that our algorithm can detect the moving objects accurately, and show the moving direction of the foreground objects, compared with the Ninad Thakoor＇s method.
出处
《计算机工程与科学》
CSCD
北大核心
2010年第1期67-70,共4页
Computer Engineering & Science
