CAJ | 학술논문

针对静止摄像机条件下运动车辆的检测问题,提出一种改进的自适应混合高斯背景模型的方法.该方法初始时通过三帧差分法判断运动目标所在区域,运用提出的区域背景更新算法生成初始背景图像,然后在Stauffer等人提出的自适应混合高斯背景模型的基础上融入帧间差分和背景差分相结合的方法用于判定运动目标区域和背景区域,通过对背景区域和运动目标区域设置不同的学习率来更新背景模型,提高了模型的收敛速度.实验结果表明,同传统检测方法相比,改进的算法能较快地初始化背景模型并能有效地检测出运动车辆,有较强的鲁棒性和较好的自适应能力.
침대정지섭상궤조건하운동차량적검측문제,제출일충개진적자괄응혼합고사배경모형적방법.해방법초시시통과삼정차분법판단운동목표소재구역,운용제출적구역배경경신산법생성초시배경도상,연후재Stauffer등인제출적자괄응혼합고사배경모형적기출상융입정간차분화배경차분상결합적방법용우판정운동목표구역화배경구역,통과대배경구역화운동목표구역설치불동적학습솔래경신배경모형,제고료모형적수렴속도.실험결과표명,동전통검측방법상비,개진적산법능교쾌지초시화배경모형병능유효지검측출운동차량,유교강적로봉성화교호적자괄응능력.
An improved adaptive background mixture Gaussian model has been proposed under the conditions of static camera motion detection problem of vehicles.Initially,it determines the moving target region by three frame difference methods,updates the algorithm by regional background to generate the initial background image.Then on the basis of adaptive Gaussian mixture background model put forward by Stauffer,it integrates the frame difference and background difference methods to determine moving target zone and background zone.It sets different learning rates according to background zone and the moving target zone to update the background model and it raises the convergence speed.The experimental results show that,compared with traditional detection methods,the improved algorithm can more quickly initialize background model and efficiently detect moving vehicles and also it has strong robustness and good adaptability.