A Deep Convolutional Neural Network for Background Subtraction

TL;DR

This paper introduces a deep CNN-based background subtraction system that automates feature learning, improves accuracy over existing methods, and operates in real-time, using minimal training data from video frames.

Contribution

The paper presents a novel CNN architecture for background subtraction that eliminates the need for manual feature engineering and achieves superior performance.

Findings

Outperforms existing algorithms in accuracy

Capable of real-time processing

Effective with limited training data

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.