  Visual change detection, aiming at segmentation of video frames into
foreground and background regions, is one of the elementary tasks in computer
vision and video analytics. The applications of change detection include
anomaly detection, object tracking, traffic monitoring, human machine
interaction, behavior analysis, action recognition, and visual surveillance.
Some of the challenges in change detection include background fluctuations,
illumination variation, weather changes, intermittent object motion, shadow,
fast/slow object motion, camera motion, heterogeneous object shapes and
real-time processing. Traditionally, this problem has been solved using
hand-crafted features and background modelling techniques. In recent years,
deep learning frameworks have been successfully adopted for robust change
detection. This article aims to provide an empirical review of the
state-of-the-art deep learning methods for change detection. More specifically,
we present a detailed analysis of the technical characteristics of different
model designs and experimental frameworks. We provide model design based
categorization of the existing approaches, including the 2D-CNN, 3D-CNN,
ConvLSTM, multi-scale features, residual connections, autoencoders and GAN
based methods. Moreover, an empirical analysis of the evaluation settings
adopted by the existing deep learning methods is presented. To the best of our
knowledge, this is a first attempt to comparatively analyze the different
evaluation frameworks used in the existing deep change detection methods.
Finally, we point out the research needs, future directions and draw our own
conclusions.
