In many computer vision applications, segmenting and extraction of moving objects in video sequences is an essential task. Background subtraction, by which each input image is subtracted from the reference image, has often been used for this purpose. In this paper, we offer a novel background-subtraction technique for real-time dynamic background generation using color images that are taken fro...

We introduce a performance evaluation methodology called Perturbation Detection Rate (PDR) analysis for measuring performance of foreground-background segmentation. It has some advantages over the commonly used Receiver Operation Characteristics (ROC) analysis. Specifically, it does not require foreground targets or knowledge of foreground distributions. It measures the sensitivity of a backgro...

A common method for real-time segmentation of moving regions in image sequences involves “background subtraction,” or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mix...

We present an approach to tracking multiple human subjects within a camera network. A particle filter framework is used in which we combine foreground-background subtraction with a novel approach to texture learning and likelihood computation based on an ellipsoid model. As there are inevitable problems with multiple subjects due to occlusion and crossing, we include a robust method to suppress...

The low-level task of foreground-background segregation is an important foundation for many high-level computer vision tasks and has been intensively researched in the past. Nonetheless, unregulated environments usually impose challenging problems, especially the difficult and often neglected underwater environment. There, among others, the edges are blurred, the contrast is impaired and the co...

We present a real-time algorithm for foreground–background segmentation. Sample background values at each pixel are quantized into codebooks which represent a compressed form of background model for a long image sequence. This allows us to capture structural background variation due to periodic-like motion over a long period of time under limited memory. The codebook representation is efficient...

This work adopts background subtraction to develop automatic extraction of moving objects in video surveillance. Pixels at a specific position of continuous image frames are manipulated by the modified iterative threshold selection technique to determine the background gray-level value. Pixels at all positions employ such an iterative technique to establish the background that is then analyzed ...