A Background Modeling Approach Based on Visual Background Extractor

Moving objects detection is an important research content in computer vision field, and it plays a very important role in many vision applications such as smart video surveillance, intelligent transportation, and human-computer interaction, traffic control, activity recognition, object tracking and behavior understanding. The visual background extractor (ViBe) is a classical approach, however, when a moving objects in the initialized frame, it will take a lot time to fuse the gost and when there braches or surface of the water in the frame, it can make more false detection. In this paper, an algorithm based on ViBe is proposed. Though frame subtraction we acquire a real background, moreover we initial the model. On the other hand, we use 3-5 frames initial the model. The improved approaches solve the problems of gost or blinking pixels. Experiment show that, the algorithm can improve the effective of detection in the specific scene with similar computer load. Introduction Moving objects detection which extracts foreground objects from the background for a static camera has been extensively studied for many years [1,2.3. 4.5. 6]. Moving objects detection plays a very important role in many vision applications with the purpose of subtracting interesting target area and locating the moving objects from image sequences. It is widely used in vision systems such as smart video surveillance, intelligent transportation, and human-computer interaction, traffic control, activity recognition, object tracking and behavior understanding. Accurate moving object detection is essential for the robustness of intelligent video-surveillance systems. Background subtraction and temporal differencing are two popular approaches for moving objects detection in an image sequence under a stationary camera. The Gaussian mixture model (GMM) [7] which is a widely used because of its self learning capacity and its robustness to variations in lighting. However, some false positive pixels will be produced in highly dynamic scenes where dynamic texture does not repeat exactly. W4 [8] use three parameters to model each pixel of background, but it generally can only recover partial edge shapes of moving objects. Li [9] use spatial information at feature level, such as color and gradient. This can improve the accuracy of the background model and is most suitable for the stationary background. Olivier Barnich and Marc Van Droogenbroeck proposed Vibe algorithm [10.11]. The method adopt neighboring pixels to create the background model, but if there is a moving object in the background when the modeling initializing there will make gost in detecting. Moreover, because of ViBe use only one frame to initialize the model, when the background have many blinking pixels which can make more false detection. In order to overcome the shortcoming of origin VIBE and provide accurate result with good spatial and temporal consistency, In this paper we present a novel moving object detection method with frame difference and 3-5 frames fuse based on VIBE to solve the problem in special scene. Firstly we obtain a real model not including moving objects present in the beginning frames which cause gost in the result of detection use the frame difference to initialize the models of background. Moreover, to decrease false detection in the scenes of surface of water or shaky branch which make a lot of blinking pixels, we make use of 3-5frames to initialize the models that includes possible pixel values of 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2015) © 2015. The authors Published by Atlantis Press 2535 blinking pixels. Experiment show that, the improved algorithm can improve the effective of detection in the specific scene with similar computer load. Visual background extractor Visual Background Extractor (ViBe) adopts neighboring pixels to establish the background model and distinguish background and foreground by comparing the background model with the current pixel value. The implementation of the algorithm which is a universal background modeling method is subdivided into three steps [11]. Background Model Initialization. The first step of ViBe is to initialize each pixel in the background model from the single frame image which is an important innovation of ViBe. As we know there is no temporal information in a single frame and it is supposed that neighboring pixels share a similar temporal distribution. This means that the value of a pixel and its neighbor pixel values in spatial domain has a similar distribution. The right size range of the neighborhood can be 4 or 8, and a suitable range can make sure that the background model includes a sufficient number of different samples, while keeping in mind that as the neighborhood scope increases, the correlation between pixel values at different locations decreases. Let’s t=0 denotes the first frame, so we can get the model as follow: { } 0 0 ( , ) ( , ) | ( , ) ( , ) n n n n m G BG x y P x y x y N x y = ∈ (1) Where 0 0 ( , ), ( , ), ( , ) G BG x y N x y P x y denotes the pixel background model value, spatial neighborhood value, pixel value respectively, and ( , ) n n x y in ( , ) G N x y is selected such as probability. m = 1, 2, . , N is the number of samples. The method of initializing of model was showed in Fig.1 Fig. 1 Initialize of ViBe Pixel Classification Process. Through the initialized model, we can use a simple decision process to determine whether a pixel of the input frame is belongs to background or not. As described previous, each background pixel was modeled with a set of samples which from the pixel’s neighbor, though compare a new value to the background samples and then determine whether the new value belongs to background or not. Formally, let us denote by ( , ) P x y the value in a given Euclidean color space taken by the pixel located at (x,y) in the image, and by ( , ) i P x y a background sample value with an index i . Each background pixel (x,y) is modeled by a collection of N background sample values { } 1 2 ( , ) ( , ), ( , ),. , ( , ) N S x y P x y P x y P x y = (2) To classify a pixel value ( , ) P x y according to its corresponding model ( , ) S x y , we compare it to the closest values within the set of samples by defining a sphere ( ( , )) R S P x y of radius R centered on ( , ) P x y . The process can be showed as Fig.2