Advanced Video and Signal-Based Surveillance

@INPROCEEDINGS{AVSS2011_1569429961,
  author = {Jacky Shun-Cho Yuk and Kwan-Yee Kenneth Wong},
  title = {{An Efficient Pattern-Less Background Modeling based on Scale Invariant
Local States}},
  booktitle = {2011 8th IEEE International Conference on 
	Advanced Video and Signal-Based Surveillancei (AVSS)},
  year = {2011},
  pages = {6},
  month = {Aug.},
  abstract = {
A robust and efficient background modeling algorithm is crucial to the
success of most of the intelligent video surveillance systems. Compared
with intensity-based approaches, texture-based background modeling
approaches have shown to be more robust against dynamic backgrounds and
illumination changes, which are common in real life videos. However, many
of the existing texture-based methods are too computationally expensive,
which renders them useless in real-time applications. In this paper, a
novel efficient texture-based background modeling algorithm is presented.
Scale invariant local states (SILS) are introduced as pixel features for
modeling a background pixel, and a pattern-less probabilistic measurement
(PLPM) is derived to estimate the probability of a pixel being background
from its SILS. An adaptive background modeling framework is also introduced
for learning and representing a multi-modal background model. Experimental
results show that the proposed method can run nearly 3 times faster than
existing state-of-the-art texture-based method, without sacrificing the
output quality. This allows more time for a real-time surveillance system
to carry out other computationally intensive analysis on the detected
foreground objects.
  }
}