  Visual surveillance aims to stably detect a foreground object using a
continuous image acquired from a fixed camera. Recent deep learning methods
based on supervised learning show superior performance compared to classical
background subtraction algorithms. However, there is still a room for
improvement in static foreground, dynamic background, hard shadow, illumination
changes, camouflage, etc. In addition, most of the deep learning-based methods
operates well on environments similar to training. If the testing environments
are different from training ones, their performance degrades. As a result,
additional training on those operating environments is required to ensure a
good performance. Our previous work which uses spatio-temporal input data
consisted of a number of past images, background images and current image
showed promising results in different environments from training, although it
uses a simple U-NET structure. In this paper, we propose a data augmentation
technique suitable for visual surveillance for additional performance
improvement using the same network used in our previous work. In deep learning,
most data augmentation techniques deal with spatial-level data augmentation
techniques for use in image classification and object detection. In this paper,
we propose a new method of data augmentation in the spatio-temporal dimension
suitable for our previous work. Two data augmentation methods of adjusting
background model images and past images are proposed. Through this, it is shown
that performance can be improved in difficult areas such as static foreground
and ghost objects, compared to previous studies. Through quantitative and
qualitative evaluation using SBI, LASIESTA, and our own dataset, we show that
it gives superior performance compared to deep learning-based algorithms and
background subtraction algorithms.

Computer Vision and Pattern Recognition