Deep learning-based algorithms showed promising prospects in the computer vision domain. However, their deployment in real-time systems is challenging due to their computational complexity, high-end hardware prerequisites, and the amount of annotated data for training. This paper proposes an efficient foreground detection (EFDNet) algorithm based on deep spatial features extracted from an RGB input image using VGG-16 convolutional neural networks (CNN). The VGG-16 CNN is modified by concatenated residual (CR) blocks to learn better global contextual features and recover lost feature information due to several convolution operations. A new upsampling network is designed using bilinear interpolation sandwiched between $3\times 3$ convolutions to upsample and refine feature maps for pixel-wise prediction. This helps to propagate loss errors from the upsampling network during backpropagation. The experiments showed the effectiveness of the EFDNet in outperforming top-ranked foreground detection algorithms. EFDNet trains faster on low-end hardware and demonstrated promising results with a minimum of 50 training frames with binary ground-truth.

中文翻译：

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用于低端视频监控系统的基于双摄像头的有监督前景检测

基于深度学习的算法在计算机视觉领域显示出令人鼓舞的前景。但是，由于它们的计算复杂性，高端硬件先决条件以及用于训练的带注释数据量，它们在实时系统中的部署具有挑战性。本文提出了一种有效的前景检测（EFDNet）算法，该算法基于使用VGG-16卷积神经网络（CNN）从RGB输入图像中提取的深层空间特征。VGG-16 CNN通过级联残差（CR）块进行了修改，以学习更好的全局上下文特征并恢复由于几次卷积操作而丢失的特征信息。使用双线性插值设计了一个新的上采样网络，将其夹在中间 3美元/次3美元 进行卷积以对像素图进行上采样并精炼特征图。这有助于在反向传播期间传播来自上采样网络的损耗错误。实验表明，EFDNet的性能优于排名靠前的前景检测算法。EFDNet在低端硬件上的训练速度更快，并通过最少50个带有二进制基础的训练框架展示了令人鼓舞的结果。