.png){kind=icon}
Return
Spatio-temporal context for codebook-based dynamic background subtraction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.aeue.2009.05.004.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In background subtraction, it is challenging to detect foreground objects in the presence of dynamic background motions. The paper proposes two new algorithms to this problem by improving the codebook model with the incorporation of the spatial and temporal context of each pixel. The spatial context involves the local spatial dependency between neighboring pixels, and the temporal context involves the preceding detection result. Only the spatial context is incorporated into the first algorithm which makes the background representation more compact than the standard codebook. The second algorithm explicitly models the spatio-temporal context with a Markov random field model, thus achieving more accurate foreground detection. Extensive experiments on several dynamic scenes are conducted to compare the two proposed algorithms with each other and with the standard codebook algorithm. (C) 2009 Elsevier GmbH. All rights reserved.
Keywords:
Dynamic background subtraction
Object detection
Codebook
Spatio-temporal context
Markov random field
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
A
IF:
3.2
Papers:
5.6K
Citations:
8.3K
Organization
Cited Papers
ShareSaveA consensus-based method for tracking: Modelling background scenario and foreground appearance.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveShareSaveShareSaveInvestigation of the efficiency of a solar dryer with thermal storage by rock salt and by pebble bed
PREAI
ShareSave
View more