  Moving object detection (MOD) is a significant problem in computer vision
that has many real world applications. Different categories of methods have
been proposed to solve MOD. One of the challenges is to separate moving objects
from illumination changes and shadows that are present in most real world
videos. State-of-the-art methods that can handle illumination changes and
shadows work in a batch mode; thus, these methods are not suitable for long
video sequences or real-time applications. In this paper, we propose an
extension of a state-of-the-art batch MOD method (ILISD) to an
online/incremental MOD using unsupervised and generative neural networks, which
use illumination invariant image representations. For each image in a sequence,
we use a low-dimensional representation of a background image by a neural
network and then based on the illumination invariant representation, decompose
the foreground image into: illumination change and moving objects. Optimization
is performed by stochastic gradient descent in an end-to-end and unsupervised
fashion. Our algorithm can work in both batch and online modes. In the batch
mode, like other batch methods, optimizer uses all the images. In online mode,
images can be incrementally fed into the optimizer. Based on our experimental
evaluation on benchmark image sequences, both the online and the batch modes of
our algorithm achieve state-of-the-art accuracy on most data sets.
