A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models

Abstract

Abstract. Copyright 2002 Springer-Verlag. Published in the 7th European Conference on Computer Vision (ECCV-2002), May 28-31, 2002, Copenhagen, Denmark. Personal Time-Adaptive, Per-Pixel Mixtures Of Gaussians (TAPPMOGs) have recently become a popular choice for robust modeling and removal of complex and changing backgrounds at the pixel level. However, TAPPMOG-based methods cannot easily be made to model dynamic backgrounds with highly complex appearance, or to adapt promptly to sudden "uninteresting" scene changes such as the re-positioning of a static object or the turning on of a light, without further undermining their ability to segment foreground objects, such as people, where they occlude the background for too long. To alleviate tradeoffs such as these, and, more broadly, to allow TAPPMOG segmentation results to be tailored to the specific needs of an application, we introduce a general framework for guiding pixel-level TAPPMOG evolution with feedback from "high-level" modules. Each such module can use pixel-wise maps of positive and negative feedback to attempt to impress upon the TAPPMOG some definition of foreground that is best expressed through "higher-level" primitives such as image region properties or semantics of objects and events. By pooling the foreground error corrections of many high-level modules into a shared, pixel-level TAPPMOG model in this way, we improve the quality of the foreground segmentation and the performance of all modules that make use of it. We show an example of using this framework with a TAPPMOG method and high-level modules that all rely on dense depth data from a stereo camera.

A Change-Detection Algorithm Enabling Intelligent Background Maintenance

“…Among the change detection algorithms relying on the background subtraction principle, the statistical approach is the most widely adopted one [2,3,4,5,6]. With this approach some features are used to represent the background pixels (e.g.…”

“…However, we extract gradient information at a reduced resolution to significantly improve robustness and explicitly exploit illuminationinsensitivity within the background maintenance process. The idea of exploiting an interaction between the change-detection level and higher-level modules can be found also in [5]. Yet, this method is much more complex since it relies on combining in a Mixtures of Gaussians framework colour information with the depth measurements provided by a stereo system.…”

“…Excluding [5], in the above mentioned algorithms a foreground object is immediately and gradually included into the background as soon as it stops moving so that, after a certain time interval, it will be perceived as background. Similarly, a ghost will be absorbed into the background according to the same dynamics.…”

Low-Rank Representation with Graph Constraints for Robust Visual Tracking

“…In recent years, model based tracking algorithms have received significant attention due to their good performance of handling appearance variability of the target object, such as 3D models [1], integration of shape and color [2], foreground/background models [3], kernel-based filters [4] and subspace learning models [5], [6]. The common part of these algorithms is that all of them build or learn a model of the target object at first and then use it for tracking.…”

Interactive Motion Analysis for Video Surveillance and Long Term Scene Monitoring

“…In order to improve the robustness and efficiency of background subtraction methods, some papers [3,5,6] introduced feedback from the frame level and some papers employed the feedback from the tracker [7][8][9][10][11]. Abbott et al [7] proposed a method to reduce computational cost in visual tracking systems by using track state estimates to direct and constrain image segmentation via background subtraction and connected components analysis.…”