Adaptive Color Background Modeling for Real-Time Segmentation of Video Streams
We present a system to perform real-time background modeling and segmentation of video streams on a PC,
in the context of video surveillance and multimedia applications.
The images, captured with a fixed camera, are modeled as a fixed or slowly changing background,
which may become occluded by mobile agents. The system learns a statistical color model
of the background, which is used for detecting changes produced by occluding elements.
We propose to operate in the Hue-Saturation-Value (HSV) color space, instead of the
traditional RGB space, and show that it provides a better use of the color information,
and naturally incorporates gray-level only processing. At each instant, the system maintains
an updated background model, and a list of occluding regions that can then be tracked.
Other applications are video compression, enhancement and modification, such as obstacle
highlight or removal.
Keywords: Image sequence processing; Adaptive background modeling; Video stream segmentation.