Background segmentation with feedback: The Pixel-Based Adaptive Segmenter

Abstract

In this paper we present a novel method for foreground segmentation. Our proposed approach follows a nonparametric background modeling paradigm, thus the background is modeled by a history of recently observed pixel values. The foreground decision depends on a decision threshold. The background update is based on a learning parameter. We extend both of these parameters to dynamic per-pixel state variables and introduce dynamic controllers for each of them. Furthermore, both controllers are steered by an estimate of the background dynamics. In our experiments, the proposed Pixel-Based Adaptive Segmenter (PBAS) outperforms most state-of-the-art methods.

Background Subtraction on Embedded Hardware

“…only shows robustness to background motion and camera jitter but also to ghosting artifacts. Hofmann [64] improved the robustness of ViBe on a variety of difficult scenarios by automatically tuning its decision threshold and learning rate based on previous decisions made by the system. In both [6,64], a pixel is declared as foreground if it is not close to a sufficient number of background samples from the past.…”

“…Hofmann [64] improved the robustness of ViBe on a variety of difficult scenarios by automatically tuning its decision threshold and learning rate based on previous decisions made by the system. In both [6,64], a pixel is declared as foreground if it is not close to a sufficient number of background samples from the past. A deterministic K nearest neighbor approach has also been proposed by Zivkovic and van der Heijiden [202], and one for non-parametric methods by Manzanera [108].…”

“…Results for data-driven methods and machine learning methods are also reported. That is Hofmann's stochastic and self-adaptive method (PBAS) [64], a simple K-nearest neighbor method [202] and neural maps methods (SOBS and SC-SOBS) by Maddalena et al [104,106] and a neural network method with a region-based Markovian post-processing methods (PSP-MRF) by Schick et al [152]. We also have results for two commercial products.…”

Practical automatic background substitution for live video

“…More recent video matting methods loosen the requirement for the background to have constant color, and only assume that the background can be pre-captured and remains static or only contains slight movements. They model the background using either generative methods, such as a Bayesian model [6], a self-organized map [7], a Gaussian mixture model [8], independent component analysis [9], a foreground-background mixture model [28], or non-parametric methods [10,11]. Such models allow prediction of the probability of a pixel belonging to the background.…”

“…Automatic approaches usually assume that the camera and background are static, and a pre-captured background image is available. They try to model the background using either generative methods [6][7][8][9], or non-parametric methods [10,11]. Those pixels which are consistent with the background model are labeled as background, and the remainder are labeled as foreground.…”