  In recent years the interest in segmentation has been growing, being used in
a wide range of applications such as fraud detection, anomaly detection in
public health and intrusion detection. We present an ablation study of
FgSegNet_v2, analysing its three stages: (i) Encoder, (ii) Feature Pooling
Module and (iii) Decoder. The result of this study is a proposal of a variation
of the aforementioned method that surpasses state of the art results. Three
datasets are used for testing: CDNet2014, SBI2015 and CityScapes. In CDNet2014
we got an overall improvement compared to the state of the art, mainly in the
LowFrameRate subset. The presented approach is promising as it produces
comparable results with the state of the art (SBI2015 and Cityscapes datasets)
in very different conditions, such as different lighting conditions.
