  Scene change detection is an image processing problem related to partitioning
pixels of a digital image into foreground and background regions. Mostly,
visual knowledge-based computer intelligent systems, like traffic monitoring,
video surveillance, and anomaly detection, need to use change detection
techniques. Amongst the most prominent detection methods, there are the
learning-based ones, which besides sharing similar training and testing
protocols, differ from each other in terms of their architecture design
strategies. Such architecture design directly impacts on the quality of the
detection results, and also in the device resources capacity, like memory. In
this work, we propose a novel Multiscale Cascade Residual Convolutional Neural
Network that integrates multiscale processing strategy through a Residual
Processing Module, with a Segmentation Convolutional Neural Network.
Experiments conducted on two different datasets support the effectiveness of
the proposed approach, achieving average overall
$\boldsymbol{F\text{-}measure}$ results of $\boldsymbol{0.9622}$ and
$\boldsymbol{0.9664}$ over Change Detection 2014 and PetrobrasROUTES datasets
respectively, besides comprising approximately eight times fewer parameters.
Such obtained results place the proposed technique amongst the top four
state-of-the-art scene change detection methods.
