A Multilayer-Based Framework for Online Background Subtraction with Freely Moving Cameras
This addresses the need for robust background subtraction in dynamic camera scenarios, offering a novel approach for video analysis applications, though it is incremental in advancing segmentation techniques.
The paper tackles the problem of background subtraction in videos from moving cameras by proposing a multilayer framework that models multiple foreground objects as a multi-label segmentation problem, achieving superior performance over state-of-the-art methods on challenging sequences.
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.