Book ChapterDOI
Adaptive Model for Background Extraction Using Depth Map
Boyuan Sun,Tammam Tillo,Ming Xu +2 more
- pp 419-427
TLDR
A new algorithm for background extraction using Gaussian Mixture Models GMM combined with depth map is presented, showing much greater robustness than prior state of the art method to handle challenging scenes.Abstract:
Depth map has attracted great attention for image and video processing in recent years Depth map gives one more dimensional information about the images besides color intensity Depth is independent of color, which is the advantage for extracting the background covered by objects with irregular repetitive motions eg rotation A new algorithm for background extraction using Gaussian Mixture Models GMM combined with depth map is presented The per-pixel mixture model and single Gaussian model are used to model the recent observation in color and depth space respectively We also incorporate the color-depth consistency check mechanism into the algorithm to improve the accuracy Our results show much greater robustness than prior state of the art method to handle challenging scenesread more
Citations
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Journal ArticleDOI
Background Subtraction for Moving Object Detection in RGBD Data: A Survey
TL;DR: Depth synchronized information acquired by low-cost RGBD sensors is considered in this paper to give evidence about which issues can be solved, but also to highlight new challenges and design opportunities in several applications and research areas.
Proceedings ArticleDOI
A refinement framework for background subtraction based on color and depth data
TL;DR: Experiments show that the method can take full advantage of the both information to detect foreground in color camouflage and shadowing situations, giving a promising result which is robust to the inaccurate initial detections, and outperforming the state-of-art algorithms that based on color and depth data.
References
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Proceedings ArticleDOI
Adaptive background mixture models for real-time tracking
Chris Stauffer,W.E.L. Grimson +1 more
TL;DR: This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model, resulting in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes.
Journal ArticleDOI
Pfinder: real-time tracking of the human body
TL;DR: Pfinder is a real-time system for tracking people and interpreting their behavior that uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions.
Book ChapterDOI
An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection
TL;DR: This paper presents a method which improves this adaptive background mixture model by reinvestigating the update equations at different phases, which allows the system learn faster and more accurately as well as adapts effectively to changing environment.
Journal ArticleDOI
Effective Gaussian mixture learning for video background subtraction
TL;DR: An effective scheme to improve the convergence rate without compromising model stability is proposed by replacing the global, static retention factor with an adaptive learning rate calculated for each Gaussian at every frame.
Proceedings ArticleDOI
Automatic congestion detection system for underground platforms
Benny Lo,Sergio A. Velastin +1 more
TL;DR: The system was tested with recorded video from the London Bridge station, and the testing results were shown to be accurate in identifying overcrowding conditions for the unique platform environment.