Deep Learning Based Background Subtraction: A Systematic Survey

Machine learning has been widely applied for detection of moving objects from static cameras. Recently, many methods using deep learning for background subtraction have been reported, with very promising performance. This chapter provides a survey of different deep-learning based background subtraction methods. First, a comparison of the architecture of each method is provided, followed by a discussion against the specific application requirements such as spatio-temporal and real-time constraints. After analyzing the strategies of each method and showing their limitations, a comparative evaluation on the large scale CDnet2014 dataset is provided. Finally, we conclude with some potential future research directions.