  Extracting moving objects from a video sequence and estimating the background
of each individual image are fundamental issues in many practical applications
such as visual surveillance, intelligent vehicle navigation, and traffic
monitoring. Recently, some methods have been proposed to detect moving objects
in a video via low-rank approximation and sparse outliers where the background
is modeled with the computed low-rank component of the video and the foreground
objects are detected as the sparse outliers in the low-rank approximation. All
of these existing methods work in a batch manner, preventing them from being
applied in real time and long duration tasks. In this paper, we present an
online sequential framework, namely contiguous outliers representation via
online low-rank approximation (COROLA), to detect moving objects and learn the
background model at the same time. We also show that our model can detect
moving objects with a moving camera. Our experimental evaluation uses simulated
data and real public datasets and demonstrates the superior performance of
COROLA in terms of both accuracy and execution time.
