The exponentially increasing use of moving platforms for video capture introduces the urgent need to develop the general background subtraction algorithms with the capability to deal with the moving background. In this paper, we propose a multilayer-based framework for online background subtraction for videos captured by moving cameras. Unlike the previous treatments of the problem, the proposed method is not restricted to binary segmentation of background and foreground, but formulates it as a multi-label segmentation problem by modeling multiple foreground objects in different layers when they appear simultaneously in the scene. We assign an independent processing layer to each foreground object, as well as the background, where both motion and appearance models are estimated, and a probability map is inferred using a Bayesian filtering framework. Finally, Multi-label Graph-cut on Markov Random Field is employed to perform pixel-wise labeling. Extensive evaluation results show that the proposed method outperforms state-of-the-art methods on challenging video sequences.
The freely moving camera introduces a movement in the projected background scene, and thus complicates the background subtraction problem. An intuitive idea to tackle such a problem is compensating the camera motion. A few pioneering works resort to estimating a homography @cite_45 that characterizes the geometric transformation of background scene between consecutive frames. Typically RANSAC @cite_18 and its variants MLESAC @cite_31 are employed to achieve robust estimation using many matches of feature points. Jin al @cite_7 model the scene as a set of planer regions where each background pixel is assumed to belong to one of these regions. Homographies are used to rectify each region to its corresponding planer representation in the model. Zamalieva al @cite_41 leverage geometric information to develop multiple transformation models, and choose one that best describes the relation between consecutive frames.
@cite_18 @cite_7 @cite_41 @cite_45 @cite_31
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A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing This paper proposes a novel multi-layer homography algorithm for background modeling from a free-moving camera. Background is composed of many planes. Different planes satisfy with different homographies which can be found by our algorithm. Each pixel except for the moving pixel definitely belongs to some plane. Rectified by the corresponding homography, each static pixel in the shared view can find its match in the previous frame. Thus, frames can be rectified to a specific viewpoint for background modeling. Experiment shows it is effective. Our approach can be used in motion detection from a free-moving camera. We introduce a new approach to perform background subtraction in moving camera scenarios. Unlike previous treatments of the problem, we do not restrict the camera motion or the scene geometry. The proposed approach relies on Bayesian selection of the transformation that best describes the geometric relation between consecutive frames. Based on the selected transformation, we propagate a set of learned background and foreground appearance models using a single or a series of homography transforms. The propagated models are subjected to MAP-MRF optimization framework that combines motion, appearance, spatial, and temporal cues; the optimization process provides the final background foreground labels. Extensive experimental evaluation with challenging videos shows that the proposed method outperforms the baseline and state-of-the-art methods in most cases. From the Publisher: A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Recent major developments in the theory and practice of scene reconstruction are described in detail in a unified framework. The book covers the geometric principles and how to represent objects algebraically so they can be computed and applied. The authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly.