A Multilayer-Based Framework for Online Background Subtraction with Freely Moving Cameras
Yizhe Zhu, Ahmed Elgammal

TL;DR
This paper introduces a multilayer framework for online background subtraction in videos from moving cameras, enabling multi-object segmentation and outperforming existing methods.
Contribution
It presents a novel multilayer approach that models multiple foreground objects and background simultaneously, using Bayesian filtering and graph-cut techniques.
Findings
Outperforms state-of-the-art methods on challenging sequences
Effectively handles multiple foreground objects in moving camera videos
Provides accurate pixel-wise multi-label segmentation
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVideo Surveillance and Tracking Methods · Advanced Image and Video Retrieval Techniques · Video Analysis and Summarization
