Title: A Deep Convolutional Neural Network for Background Subtraction,
Abstract: In this work, we present a novel background subtraction system that uses a
deep Convolutional Neural Network (CNN) to perform the segmentation. With this
approach, feature engineering and parameter tuning become unnecessary since the
network parameters can be learned from data by training a single CNN that can
handle various video scenes. Additionally, we propose a new approach to
estimate background model from video. For the training of the CNN, we employed
randomly 5 percent video frames and their ground truth segmentations taken from
the Change Detection challenge 2014(CDnet 2014). We also utilized
spatial-median filtering as the post-processing of the network outputs. Our
method is evaluated with different data-sets, and the network outperforms the
existing algorithms with respect to the average ranking over different
evaluation metrics. Furthermore, due to the network architecture, our CNN is
capable of real time processing.