Proceedings ArticleDOI
Region-Based Statistical Background Modeling for Foreground Object Segmentation
K. O. De Beeck,Irene Yu-Hua Gu,L. Li,Mats Viberg,B. De Moor +4 more
- pp 3317-3320
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TLDR
A novel region-based scheme for dynamically modeling time-evolving statistics of video background, leading to an effective segmentation of foreground moving objects for a video surveillance system through introducing dynamic background region merging and splitting.Abstract:
This paper proposes a novel region-based scheme for dynamically modeling time-evolving statistics of video background, leading to an effective segmentation of foreground moving objects for a video surveillance system. In (L. Li et al., 2004) statistical-based video surveillance systems employ a Bayes decision rule for classifying foreground and background changes in individual pixels. Although principal feature representations significantly reduce the size of tables of statistics, pixel-wise maintenance remains a challenge due to the computations and memory requirement. The proposed region-based scheme, which is an extension of the above method, replaces pixel-based statistics by region-based statistics through introducing dynamic background region (or pixel) merging and splitting. Simulations have been performed to several outdoor and indoor image sequences, and results have shown a significant reduction of memory requirements for tables of statistics while maintaining relatively good quality in foreground segmented video objects.read more
Citations
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Journal ArticleDOI
NIC: A Robust Background Extraction Algorithm for Foreground Detection in Dynamic Scenes
TL;DR: In this article, a neighbor-based intensity correction (NIC) algorithm is proposed to detect the motion pixels from the difference of the background and the current frame, which is based on the comparison of the standard deviation values calculated from two pixel windows.
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A robust framework for joint background/foreground segmentation of complex video scenes filmed with freely moving camera
TL;DR: A robust region-based general framework for discriminating between background and foreground objects within a complex video sequence that can be used under difficult conditions such as dynamic background, nominally moving camera and shadows.
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Locally Statistical Dual-Mode Background Subtraction Approach
TL;DR: The proposed LSD method, namely locally statistical dual-mode (LSD), for detecting moving objects in video-based surveillance systems demonstrates its preeminence to the many state-of-the-art background subtraction approaches in terms of segmentation accuracy and computational complexity.
Book ChapterDOI
Efficient adaptive background subtraction based on multi-resolution background modelling and updating
TL;DR: This paper proposes an efficient methodology for implementation of ABS algorithms based on multiresolution background modelling and sequential sampling for updating background and shows that the proposed method requires a significant reduction in memory and CPU usage while maintaining a similar foreground segmentation performance as compared with the corresponding single resolution methods.
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Image Processing
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