Object Detection Combining Brightness Feature Autocorrelation and Gaussian Mixture Models
Simin Wang · Jisuanji gongcheng · 2014
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.