Background subtraction is an important step in object detection for many computer vision applications

including intelligent surveillance and human detection. The purpose of this process is to segment moving objects from complex scenes. Performance mainly depends on the background modeling algorithm; however

the background is a complex environment that usually includes distracting motions. Thus

background subtraction is complicated

and an adaptive method is proposed to address this problem.The method is based on the Gaussian mixture model. In their approach

each pixel is modeled by a mixture of K Gaussian distributions. An online learning technique is employed to update background models. In their approach

online K-means is applied to initialize the parameters of the Gaussian mixture model. The number of Gaussian distributions cannot be changed in the process of detection. The initialization of the model parameters significantly influences foreground detection

and the fixed Gaussian distribution cannot accommodate the changing background. In this study

we initialize the parameters of the Gaussian mixture model by combining the online K-means and the online expectation-maximization (EM) algorithms. The outcome of online K-means algorithm is the input of the online EM algorithm. The former rapidly generates the parameter values that are close to the reasonable value

whereas the online EM rapidly and accurately astringes the result that is obtained through online K-means to a reasonable range. In addition

this paper also presents a gray-value classification algorithm to adjust the number of Gaussians to adapt to the dynamic environment. Recent statistics regarding gray value are obtained for each pixel. Then

this algorithm classifies these gray values into different categories. Finally

this method updates the number of Gaussian distributions on the basis of the number of categories. In this paper

we conduct several experiments with four video datasets to evaluate the proposed background subtraction algorithm. Three of these datasets are standard test videos that are widely used in the video monitoring field. The videos entitled “Waving Trees” and “Bootstrapping” are derived from the Wallflower dataset. The pedestrian video is selected from the Change detection dataset. Another video is captured by a local bus station downtown. Moreover

a quantitative analysis is conducted with the general evaluation criteria of Precision and Recall.In the conventional Gaussian mixture model

the number of Gaussian distributions is fixed for each pixel and the system cannot adjust to the changing background. Some static regions apply only one or two Gaussian distributions

whereas moving regions need more Gaussian distributions to maintain the model. However

maintaining additional distributions consumes resources

whereas a lack of distributions may result in false detections.Test results show that the proposed method performs better than the conventional Gaussian mixture model. Experimental findings also suggest that the proposed approach adapts more effectively to complex scenes than those presented in reviewed literature. The background can be segmented effectively and rapidly

and the results for Precision-to-Recall ratio demonstrate the superiority of the method to analogous algorithms. The proposed approach provides a new direction for research on background subtraction. In future work

we will attempt to control learning rate with effective strategies and improve Precision-to-Recall ratio.