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Learning multi-scale features for foreground segmentation
Pattern Analysis and Applications ( IF 3.9 ) Pub Date : 2019-08-31 , DOI: 10.1007/s10044-019-00845-9
Long Ang Lim , Hacer Yalim Keles

Foreground segmentation algorithms aim at segmenting moving objects from the background in a robust way under various challenging scenarios. Encoder–decoder-type deep neural networks that are used in this domain recently perform impressive segmentation results. In this work, we propose a variation of our formerly proposed method (Anonymous 2018) that can be trained end-to-end using only a few training examples. The proposed method extends the feature pooling module of FgSegNet by introducing fusion of features inside this module, which is capable of extracting multi-scale features within images, resulting in a robust feature pooling against camera motion, which can alleviate the need of multi-scale inputs to the network. Sample visualizations highlight the regions in the images on which the model is specially focused. It can be seen that these regions are also the most semantically relevant. Our method outperforms all existing state-of-the-art methods in CDnet2014 datasets by an average overall F-measure of 0.9847. We also evaluate the effectiveness of our method on SBI2015 and UCSD Background Subtraction datasets. The source code of the proposed method is made available at https://github.com/lim-anggun/FgSegNet_v2.

中文翻译:

学习多尺度特征以进行前景分割

前景分割算法旨在在各种挑战性场景下以强大的方式从背景分割运动对象。最近在此领域中使用的编码器-解码器类型的深度神经网络执行了令人印象深刻的分割结果。在这项工作中,我们提出了我们先前提出的方法(Anonymous 2018)的一种变体,可以仅使用一些训练示例进行端到端的训练。所提出的方法通过在该模块内部引入特征融合来扩展FgSegNet的特征池模块,该功能能够提取图像中的多尺度特征,从而实现了针对相机运动的强大特征池,从而可以缓解多尺度需求输入到网络。样本可视化突出显示了图像中模型特别关注的区域。可以看出,这些区域在语义上也最相关。我们的方法以0.9847的平均总体F值优于CDnet2014数据集中所有现有的最先进方法。我们还评估了我们的方法在SBI2015和UCSD背景扣除数据集上的有效性。提议的方法的源代码可从https://github.com/lim-anggun/FgSegNet_v2获得。
更新日期:2019-08-31
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