摘要： 基于混合高斯模型(GMM)的背景建模算法被广泛运用于运动目标检测，但在一些发生快速光照变化的视频序列中，不能正确地检测出运动目标。此外在对GMM参数进行初始化时，若初始化图像中存在运动目标，则目标检测的结果会出现初始化图像中的运动目标，从而导致误检测。针对上述问题，提出一种基于亮度特征自相关的GMM算法，该算法根据亮度特征自相关参数判断初始化图像中是否存在运动目标，利用亮度特征自相关参数的拟合值判断当前帧是否发生快速光照变化，运用GMM和亮度差值相结合进行目标检测。对实际摄取的视频进行仿真实验，结果证明，该算法在GMM初始化图像存在运动目标的干扰条件下，能够较好地从发生快速光照变化的视频序列中提取出运动目标，满足准确性和实时性的要求。
关键词:
背景建模,
混合高斯模型,
自相关,
亮度特征,
像素匹配
Abstract: The background modeling algorithm based on Gaussian Mixture Models(GMM) is used widely in moving objects detection, but it can not accurately detect moving objects in some video sequences that have rapid changes of light. Moreover, in the initialization of GMM parameters, the result of object detection contains the moving objects of the initialization image and leads to error detection if the initialization image has moving objects. In allusion to the problems mentioned above, a GMM algorithm based on the intensity feature autocorrelation is proposed. The brightness feature autocorrelation parameters are used to identify whether there is a moving object in the initialization image, the fit value of intensity feature autocorrelation parameters is used to identify that there is a fast illumination variation or not in the current frame, and the object detection is made by using the ideas of GMM and intensity difference. The video taken actually is simulated by using the proposed algorithm that is of high accuracy and of high real-time, and results show that a moving object is extracted well from video sequences that have rapid changes of light under the disturbed condition that the initialization image of GMM has moving objects.
Key words:
background modeling,
Gaussian Mixture Models(GMM),
autocorrelation,
brightness feature,
pixel matching
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