.png){kind=icon}
返回
Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers
.png){kind=icon}
OA
AI
DOI：10.1016/j.patrec.2013.09.022.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the recent years, the computer vision community has shown great interest on depth-based applications thanks to the performance and flexibility of the new generation of RGB-D imagery. In this paper, we present an efficient background subtraction algorithm based on the fusion of multiple region-based classifiers that processes depth and color data provided by RGB-D cameras. Foreground objects are detected by combining a region-based foreground prediction (based on depth data) with different background models (based on a Mixture of Gaussian algorithm) providing color and depth descriptions of the scene at pixel and region level. The information given by these modules is fused in a mixture of experts fashion to improve the foreground detection accuracy. The main contributions of the paper are the region-based models of both background and foreground, built from the depth and color data. The obtained results using different database sequences demonstrate that the proposed approach leads to a higher detection accuracy with respect to existing state-of-the-art techniques. (C) 2013 Elsevier B.V. All rights reserved.
Keyword:
Region-based background modeling
Foreground prediction
Mixture of Gaussians
Mixture of experts
Mean shift
RGB-D cameras
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
机构
引用论文
.png){kind=icon}
分享收藏POTENTIAL IMAGING IN QUANTUM HALL DEVICES BY OPTICAL FIBER BASED POCKELS MEASUREMENT量子霍尔器件中基于光纤普克尔斯测量的势能成像
PREAI
分享收藏
查看更多内容