摘要
本文研究摄像机和目标同时运动情况下的实时目标提取问题。首先运用背景差方法,检测出静止摄像机下的运动区域,为了克服连通域分析法耗时长的不足,提出重心偏移迭代法快速获得感兴趣运动目标。在改进Camshift跟踪算法中,提出采用Bayesian概率法则在由Kalman滤波器预测的感兴趣区域(ROI)内获取颜色概率密度分布图像(CPDDI),引入即时背景(IB)以抑制背景特征。提出依据跟踪结果进行目标提取的方法,即结合CPDDI特征,并辅以适当的形态学滤波策略,从跟踪结果中提取出运动摄像机下的运动目标,解决目标被动态背景干扰的问题。实验结果表明,提出的算法能够较稳定和完整地提取出运动摄像机下的运动目标,对复杂动态背景的适应性较强,且算法完全达到了实时的运行速度。
How to extract moving object in the case of moving camera is researched in this paper. At first, the background subtraction is implemented to detect the moving region in the case of motionless camera, and then the interested moving object is fast segmented from the detected moving region with the proposed method of barycenter shift iteration instead of the time-consuming method of connected components analysis. In the enhanced Camshift tracking algorithm, the Color Probability Density Distribution Image （CPDDI） in the Region of Interest （ROI） predicted by the Kalman filter is obtained, and Instantaneous Background （IB） is introduced to suppress background features. A novel method is suggested to extract the moving object from the tracking result combining CPDDI with proper morphologic filter strategy in the case of moving camera, which can resist the disturbance from the background. Experiments show that the proposed method can extract intact moving objects stably in the case of moving camera, and can well deal with dynamic complex background. The system is computationally efficient and can run in real-time speed.
出处
《光电工程》
CAS
CSCD
北大核心
2009年第10期1-5,11,共6页
Opto-Electronic Engineering
基金
浙江省教育厅科研计划项目(Y200804686)
