视频前景运动跟踪和感兴趣区域捕获

Foreground motion tracking and ROI capturing in video

针对传统的运动检测算法不能很好地适应前景中物体运动的问题,设计了一种视频前景运动跟踪和感兴趣区域(ROI)捕获方法.视频预处理使用高斯模糊消除视频中的噪声,然后使用形态学处理操作结合基于Sobel边缘检测算法的优化轮廓搜索算法初步定位和跟踪ROI;随后平滑位置变换以识别目标的运动模式,选择有效的ROI;最后基于肤色状态获得更准确的ROI及其定位.实验结果表明:视频中的行为判断准确率和ROI捕获准确率分别达到77.5%和91.0%,能对ROI进行有效跟踪捕获;在单帧中最耗时模块的运行时间为0.006 s,满足实时性要求.

To solve the problem that traditional motion detection algorithms cannot adapt well to the motion of targets in the foreground,a method for foreground motion tracking and region of interest(ROI)capturing in video was designed.The Gaussian blur was used in video preprocessing to eliminate noise.And then the ROI was preliminary located and tracked using morphological processing operations combined with an optimized contour search algorithm,which was based on Sobel edge detection algorithm.The position transformation was then smoothed to identify the motion pattern of the target,based on which effective ROI was selected.Finally,a more accurate ROI and its position were achieved based on skin state.The experimental results show that the behavior judgment accuracy and the ROI capture accuracy in the videos reach 77.5%and 91.0%,respectively,which can effectively track and capture the ROI.The running time of the most time-consuming module in a single frame is about 0.006 s,which meets the real-time requirements of this paper.