中文摘要：

针对自然环境下运动目标检测时相机抖动问题,该文提出一种背景自适应方案。首先用Harris算子检测背景帧和当前帧感兴趣区域的角点,并在小范围内采用相关法和松弛法获取若干稳定的匹配点对。然后通过匹配点对的偏移量来估计相机的抖动参数,恢复出与当前帧匹配的背景帧。最后使用基于多分辨率金字塔模型的背景差分算法来检测运动目标,去除环境中的动态背景噪声和图像模糊引入的较小的相机偏移量估计误差。用公共测试图像的相机抖动序列对该算法进行了验证,并与当前较为先进的算法定性和定量地进行了比较,实验结果表明,该算法可以有效地解决自然环境下相机抖动问题,检测效果评价参数优于当前的算法。

英文摘要：

According to the problem of camera jittering under natural environments when detecting moving objects,a background adaptive scheme is proposed in the paper.First,the Harris operator is used to detect corners in the region-of-interest for background and foreground image respectively.A correlation and relaxation method is also applied to a small region to obtain several stable matched points.Then,the camera jitter parameter is estimated with offsets of these matched points and used to recover background image to match against the current image.At last,background difference algorithm based on the multi-resolution pyramid is adopted to detect moving object.It can remove the environment dynamic background noises and some small offset estimation errors caused by image blurring.The proposed algorithm is verified with camera jittering sequence of the public test image and compared with several state-of-the-art algorithms qualitatively and quantitatively.Experimental results demonstrate that the proposed algorithm can solve the problem of camera jittering in natural environment effectively.The detected effect evaluation parameter is better than the current algorithms.