CAJ | 학술논문

背景图像差分法是运动目标实时检测中常用的方法，但缺乏背景图像随监视场景光照变化而及时更新的合理方法，限制了该方法的适应性。对此，文章首先提出了一种自适应背景更新方法；然后利用最大类间方差法实现运动目标的自适应阈值分割，并利用基于形态学方法的连通区检测算法检测运动目标；最后以Kalman滤波为运动模型实现对运动目标的连续跟踪。实验结果表明：所提方法可随着光照条件的变化，实时、准确地检测出运动目标并实现稳定跟踪。
배경도상차분법시운동목표실시검측중상용적방법，단결핍배경도상수감시장경광조변화이급시경신적합리방법，한제료해방법적괄응성。대차，문장수선제출료일충자괄응배경경신방법；연후이용최대류간방차법실현운동목표적자괄응역치분할，병이용기우형태학방법적련통구검측산법검측운동목표；최후이Kalman려파위운동모형실현대운동목표적련속근종。실험결과표명：소제방법가수착광조조건적변화，실시、준학지검측출운동목표병실현은정근종。
The method based on background image difference is commonly used for real-time detection of moving object. However, no reasonable method has been designed for automatic background updating along with the illumination variance, which limits its applications. To overcome the problem, a new self-adaptive background updating algorithm was first presented in this paper, and then self-adaptive threshold segmentation of moving objects was implemented using the Otsu method, and moving objects were detected by the binary connected component analysis using the morphological method, and Katman filtering was used for objects tracking. Experimental results demonstrated that the proposed method could detect and track moving objects exactly and quickly along with the variance of illumination.