  Visual surveillance aims to perform robust foreground object detection
regardless of the time and place. Object detection shows good results using
only spatial information, but foreground object detection in visual
surveillance requires proper temporal and spatial information processing. In
deep learning-based foreground object detection algorithms, the detection
ability is superior to classical background subtraction (BGS) algorithms in an
environment similar to training. However, the performance is lower than that of
the classical BGS algorithm in the environment different from training. This
paper proposes a spatio-temporal fusion network (STFN) that could extract
temporal and spatial information using a temporal network and a spatial
network. We suggest a method using a semi-foreground map for stable training of
the proposed STFN. The proposed algorithm shows excellent performance in an
environment different from training, and we show it through experiments with
various public datasets. Also, STFN can generate a compliant background image
in a semi-supervised method, and it can operate in real-time on a desktop with
GPU. The proposed method shows 11.28% and 18.33% higher FM than the latest deep
learning method in the LASIESTA and SBI dataset, respectively.
