.png){kind=icon}
.png){kind=icon}
Return
Universal Multimode Background Subtraction
.png){kind=icon}
OA
AI
DOI：10.1109/TIP.2017.2695882.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we present a complete change detection system named multimode background subtraction. The universal nature of system allows it to robustly handle multitude of challenges associated with video change detection, such as illumination changes, dynamic background, camera jitter, and moving camera. The system comprises multiple innovative mechanisms in background modeling, model update, pixel classification, and the use of multiple color spaces. The system first creates multiple background models of the scene followed by an initial foreground/background probability estimation for each pixel. Next, the image pixels are merged together to form mega-pixels, which are used to spatially denoise the initial probability estimates to generate binary masks for both RGB and YCbCr color spaces. The masks generated after processing these input images are then combined to separate foreground pixels from the background. Comprehensive evaluation of the proposed approach on publicly available test sequences from the CDnet and the ESI data sets shows superiority in the performance of our system over other state-of-the-art algorithms.
Keywords:
Computer vision
change detection
background model bank
background subtraction
color spaces
binary classifiers
foreground segmentation
pixel classification
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