  Video segmentation consists of a frame-by-frame selection process of
meaningful areas related to foreground moving objects. Some applications
include traffic monitoring, human tracking, action recognition, efficient video
surveillance, and anomaly detection. In these applications, it is not rare to
face challenges such as abrupt changes in weather conditions, illumination
issues, shadows, subtle dynamic background motions, and also camouflage
effects. In this work, we address such shortcomings by proposing a novel deep
learning video segmentation approach that incorporates residual information
into the foreground detection learning process. The main goal is to provide a
method capable of generating an accurate foreground detection given a grayscale
video. Experiments conducted on the Change Detection 2014 and on the private
dataset PetrobrasROUTES from Petrobras support the effectiveness of the
proposed approach concerning some state-of-the-art video segmentation
techniques, with overall F-measures of $\mathbf{0.9535}$ and $\mathbf{0.9636}$
in the Change Detection 2014 and PetrobrasROUTES datasets, respectively. Such a
result places the proposed technique amongst the top 3 state-of-the-art video
segmentation methods, besides comprising approximately seven times less
parameters than its top one counterpart.
