CAJ | 학술논문

鉴于目前基于图像的运动目标检测系统对目标场景光照条件非常敏感，文中提出一种基于Kinect深度数据曲率的单高斯模型运动目标检测方法，增强了系统对场景采集误差的鲁棒性。首先对深度数据进行中值滤波，利用单高斯模型对目标区域深度数据进行建模；在对目标场景实时采集数据与背景参数进行高斯概率门限值判别后，经过形态学滤波，达到了运动目标检测的目的。同时利用实时更新背景参数的方法提高模型适应场景变化的能力，并通过实验取得了良好的检测效果。
감우목전기우도상적운동목표검측계통대목표장경광조조건비상민감，문중제출일충기우Kinect심도수거곡솔적단고사모형운동목표검측방법，증강료계통대장경채집오차적로봉성。수선대심도수거진행중치려파，이용단고사모형대목표구역심도수거진행건모；재대목표장경실시채집수거여배경삼수진행고사개솔문한치판별후，경과형태학려파，체도료운동목표검측적목적。동시이용실시경신배경삼수적방법제고모형괄응장경변화적능력，병통과실험취득료량호적검측효과。
The moving target detection systems based on image processing are sensitive to the light conditions of target scene. Present a new single Gauss model moving target detection method based on Kinect depth data,which can enhance robustness of the system in scene collection error. First,median filtering is used to process depth data. Then the single Gauss model is established for the depth data of target scene. The Gauss probability threshold is used to discriminate the collecting data of target scene and background parameters. Moving tar-get detection is achieved after morphological filtering. And real-time updating background parameters is a good way to make the model a-dapt to the change of scene. At last,experiments are performed. The proposed method has achieved well detection results.