A deep convolutional neural network for video sequence background subtraction
A deep convolutional neural network for video sequence background subtraction
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DOI：
10.1016/j.patcog.2017.09.040
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发表时间：
2018-04-01
影响因子：
8
通讯作者：
Rigoll, Gerhard
中科院分区：
文献类型：
--
作者：
Babaee, Mohammadreza;Duc Tung Dinh;Rigoll, Gerhard
在这项工作中，我们提出了一种新的视频序列背景减除算法，该算法使用深度卷积神经网络（CNN）来执行分割。通过这种方法，特征工程和参数调整变得不必要，因为可以通过训练可以处理各种视频场景的单个CNN来从数据中学习网络参数。此外，我们提出了一种新的方法来估计视频序列的背景模型。对于CNN的训练，我们随机使用了5%的视频帧及其从Change Detection Challenge 2014（CDnet 2014）中获取的地面实况分割。我们还利用空间中值滤波作为网络输出的后处理。我们的方法使用不同的数据集进行评估，它（所谓的DeepBS）在CDnet 2014中宣布的不同评估指标的平均排名方面优于现有算法。此外，由于网络架构，我们的CNN能够进行真实的时间处理。(C)2017爱思唯尔有限公司版权所有
In this work, we present a novel background subtraction from video sequences algorithm 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 sequences. For the training of the CNN, we employed randomly 5% 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 it (so-called DeepBS) outperforms the existing algorithms with respect to the average ranking over different evaluation metrics announced in CDnet 2014. Furthermore, due to the network architecture, our CNN is capable of real time processing. (C) 2017 Elsevier Ltd. All rights reserved.