.png){kind=icon}
.png){kind=icon}
Return
Universal Background Subtraction Using Word Consensus Models
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2016.2598691.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Background subtraction is often used as the first step in video analysis and smart surveillance applications. However, the issue of inconsistent performance across different scenarios due to a lack of flexibility remains a serious concern. To address this, we propose a novel non-parametric, pixellevel background modeling approach based on word dictionaries that draws from traditional codebooks and sample consensus approaches. In this new approach, the importance of each background sample (or word) is evaluated online based on their recurrence among all local observations. This helps build smaller pixel models that are better suited for long-term foreground detection. Combining these models with a frame-level dictionary and local feedback mechanisms leads us to our proposed background subtraction method, coined PAWCS. Experiments on the 2012 and 2014 versions of the ChangeDetection. net data set show that PAWCS outperforms 26 previously tested and published methods in terms of overall F-Measure as well as in most categories taken individually. Our results can be reproduced with a C++ implementation available online.
Keywords:
Video segmentation
word consensus
change detection
background subtraction
video signal processing
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W