运动目标检测算法在视频监控等领域应用广泛,但是现实场景中由于噪音、光照变化等因素导致背景复杂多变,传统的运动目标检测算法往往效果不佳.为了提升算法效果,提出了一种新的基于深度编解码网络的运动目标检测算法,将问题转化为像素级的语义分割问题.事先使用大量数据离线训练出一个编解码网络,来学习背景与视频帧之间的差异性,实际应用中首先使用高斯混合模型进行背景建模,之后将所得背景与视频帧作为网络输入即可直接获取检测结果.该方法利用了深度卷积网络在抗噪及特征学习等方面的优点,无需进行复杂的参数调优即可实现高性能的运动目标检测.我们在CDnet2014数据集上进行了实验评估,实验结果显示我们所提出的算法较原GMM算法有很大提升,甚至在一些场景中的表现优于现有的一些顶尖算法.另外得益于非常简单的背景建模方法以及网络结构,我们的算法在使用GPU的情况下能够近乎实时地进行运动目标检测,实用性很强.%Moving object detection algorithms are widely used in video surveillance and other fields. But due to noise, illumination changes and other interference, traditional algorithms are often ineffective. To get a better performance, we transform the problem into a pixel-wise segmentation problem, and propose a novel algorithm based on deep encoder-decoder neural networks. We train an encoder-decoder network offline to learn the differences between the background and the video frame. We firstly use the Gaussian Mixture Model (GMM) to generate a background, and then feed video frames and the background into the encoder-decoder network to get detection results. This method utilizes the advantages of deep convolution network in anti-noise and feature learning, and performs well without complicated parameter tuning. We experiment on the CDnet2014 dataset, and results show that the algorithm we propose performs much better than the original GMM algorithm, and even outperforms some top algorithms in some scenes. Due to the simple network architecture, our algorithm is capable of almost real-time processing using a GPU, which shows its great practicality.
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