Combining Background Subtraction Algorithms with Convolutional Neural Network

TL;DR

This paper introduces a novel approach that combines multiple background subtraction algorithms using a neural network to improve foreground object detection in challenging conditions, outperforming individual methods.

Contribution

The paper proposes a new neural network-based fusion method that integrates various background subtraction algorithms for more robust foreground detection.

Findings

Outperforms individual background subtraction algorithms on CDnet 2014 dataset

More efficient than other combination strategies

Demonstrates robustness under challenging conditions

Abstract

Accurate and fast extraction of foreground object is a key prerequisite for a wide range of computer vision applications such as object tracking and recognition. Thus, enormous background subtraction methods for foreground object detection have been proposed in recent decades. However, it is still regarded as a tough problem due to a variety of challenges such as illumination variations, camera jitter, dynamic backgrounds, shadows, and so on. Currently, there is no single method that can handle all the challenges in a robust way. In this letter, we try to solve this problem from a new perspective by combining different state-of-the-art background subtraction algorithms to create a more robust and more advanced foreground detection algorithm. More specifically, an encoder-decoder fully convolutional neural network architecture is trained to automatically learn how to leverage the characteristics of different algorithms to fuse the results produced by different background subtraction algorithms and output a more precise result. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that the proposed method outperforms all the considered single background subtraction algorithm. And we show that our solution is more efficient than other combination strategies.