针对自然环境中因摄像机抖动造成无法准确检测运动目标的问题, 提出一种结合分块灰度投影、背景差分与连续帧间差分法的运动目标检测算法。该算法通过将图像帧进行分块处理, 结合离散化决策机制去除灰度梯度变化低及存在局部运动的目标区域, 提高全局运动矢量估计精度。根据块区域灰度投影曲线进行互相关计算, 完成抖动序列校正。通过对校正后的序列帧提出使用背景差分与连续三帧差分法的融合策略处理, 增强运动目标区域。通过将融合差分图像平滑处理并使用Otsu法进行自适应阈值分割, 检测前景运动目标。用公共抖动视频序列实验, 并与不同算法对比验证后可得：该算法可以准确检测出摄像机抖动场景中运动目标, 保证较好检测效果的同时检测速度较快。

To solve the problem that jitter video moving object detection is not accurate, we propose a moving object detection method based on block gray projection, background difference and continuous inter-frame difference. By dividing the image frames into block processing, the algorithm combines the discrete decision mechanism to remove the target regions with low gray gradient and the local motion, so as to improve the accuracy of global motion vector estimation. A cross correlation calculation is done for the gray projection of the block area and the image correction is completed. For the corrected sequence frames, we propose a fusion strategy based on background difference and continuous three frame difference method, which can deal with and enhance the moving target area. The selfadaptive thresholding segmentation for the differential image smoothing fusion processing and Otsu method is used to detect foreground moving targets. In jitter video sequence experiment, compared with different algorithms, the proposed algorithm can effectively detect moving targets in the jitter scene, and ensure better detection results and faster detecting speed.