# A Deep Convolutional Neural Network for Background Subtraction

arXiv: https://arxiv.org/abs/1702.01731 · HTML: https://mlsanity.com/paper/1702.01731/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2017.

## Built on by (most cited first, 5 of 5)

- [Transmission-Guided Bayesian Generative Model for Smoke Segmentation](https://mlsanity.com/paper/2303.00900/) (2023)
- [Reducing False Alarms in Video Surveillance by Deep Feature Statistical Modeling](https://mlsanity.com/paper/2307.04159/) (2023)
- [Generating Annotated Training Data for 6D Object Pose Estimation in Operational Environments with Minimal User Interaction](https://mlsanity.com/paper/2103.09696/) (2021)
- [Computational models of object motion detectors accelerated using FPGA technology](https://mlsanity.com/paper/2310.06842/) (2023)
- [Decomposer: Semi-supervised Learning of Image Restoration and Image Decomposition](https://mlsanity.com/paper/2311.16829/) (2023)

## Abstract (arXiv)

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.