Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 1) BACKGROUND SUBTRACTION METHODS <s> Identifying moving objects is a critical task for many computer vision applications; it provides a classification of the pixels into either foreground or background. A common approach used to achieve such classification is background removal. Even though there exist numerous of background removal algorithms in the literature, most of them follow a simple flow diagram, passing through four major steps, which are pre-processing, background modelling, foreground de- tection and data validation. In this paper, we survey many existing schemes in the literature of background removal, sur- veying the common pre-processing algorithms used in different situations, presenting different background models, and the most commonly used ways to update such models and how they can be initialized. We also survey how to measure the performance of any moving object detection algorithm, whether the ground truth data is available or not, presenting per- formance metrics commonly used in both cases. <s> BIB001 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 1) BACKGROUND SUBTRACTION METHODS <s> Background subtraction is one of the key techniques for automatic video analysis, especially in the domain of video surveillance. Although its importance, evaluations of recent background subtraction methods with respect to the challenges of video surveillance suffer from various shortcomings. To address this issue, we first identify the main challenges of background subtraction in the field of video surveillance. We then compare the performance of nine background subtraction methods with post-processing according to their ability to meet those challenges. Therefore, we introduce a new evaluation data set with accurate ground truth annotations and shadow masks. This enables us to provide precise in-depth evaluation of the strengths and drawbacks of background subtraction methods. <s> BIB002
Background subtraction (also named as background removal) based methods refer to a group of methods which aim at distinguishing between background and foreground motions in video sequences utilizing a background model BIB002 . VOLUME 6, 2018 Each video frame is compared against this background model or a reference, where pixels deviate significantly from the background are recognized as moving objects BIB001 . An example flow chart of background subtraction is shown in Fig. 3 . Background subtraction based methods have been recognized as one of the most widely used techniques for object segmentation in either stationary camera videos or moving camera videos. Other than camera motion compensation, background subtraction usually develops a background model based on the color, texture information of images and compare each frame with this other than relying on a camera motion model. Existing background subtraction based methods differ with each other in the image representation, background models, segmentation strategies, and so on. They can be generally divided into spatial and spatiotemporal methods. 