This paper proposes an accurate and fast background subtraction technique for object tracking in still camera videos. Regions of motion in a frame are first estimated by comparing the current frame to a previous one. A samplingresampling based Bayesian learning technique is then used on the estimated regions to perform background subtraction and accurately determine the exact pixels which corre...

A soft X-ray excess has been claimed to exist in and around a number of galaxy clusters and this emission has been attributed to the warm-hot intergalactic medium that may constitute most of the baryons in the local universe. We have re-examined a study of the XMM-Newton observations on this topic by Kaastra et al. (2003) and find that the X-ray excess (or deficit) depends upon Galactic latitud...

Scene changes such as moved objects, parked vehicles, or opened/closed doors need to be carefully handled so that interesting foreground targets can be detected along with the short-term background layers created by those changes. A simple layered modeling technique is embedded into a codebook-based background subtraction algorithm to update a background model. In addition, important issues rel...

This paper presents a novel method MGARS to automatic road area segmentation based on motion and gray feature for the purpose of urban traffic surveillance. The proposed method can locate road region by region growing algorithm with the fusion feature of motion information and grayscale of background image, which is independent to road marker information. An adaptive background subtraction appr...

We provide a fast algorithm to perform image-based tracking, which relies on the selective integration of a small subset of pixels that contain a lot of information about the state variables to be estimated. The resulting dramatic decrease in the number of pixels to process results in a substantial speedup of the basic tracking algorithm. We have used this new method within a surveillance appli...

Motivation: Background subtraction remains a necessary step for activity monitoring. An accurate representation of the background is important to clearly detect foreground objects of interest in a scene. We believe that by utilizing a single mixture of gaussians for all the pixels in an scene we can achieve a more robust model of the background. Figure 1 shows that there is significant structur...

In this paper the definitions and the properties of a newle dedicated set of highfrequency filters based on smoothing-and-clipping are briefly described. New applications for reduction of wide–field 2048×2048 CCD spectral and direct images of a new deep survey KISS are also presented.

Background subtraction is widely used for detecting moving objects; however, color similarity between a background and a moving object is still an important problem. In this paper, we present an exemplar based approach to efficiently detect walking persons that are similar in color to the background. Exemplar database is constructed from couples of degraded and complete human silhouette pattern...

Background subtraction is a vital step in many computer vision systems. In background subtraction, one is given two (or more) frames of a video sequence taken with a still camera. Due to the stationarity of the camera, any color change in the scene is mainly due to the presence of moving objects. The goal of background subtraction is to separate the moving objects (also called the foreground) f...

Background subtraction is an important first step for video analysis, where it is used to discover the objects of interest for further processing. Such an algorithm often consists of a background model and a regularisation scheme. The background model determines a perpixel measure of if a pixel belongs to the background or the foreground, whilst the regularisation brings in information from adj...