Pedestrian lane detection is an important task in many assistive and autonomous navigation systems. This article presents a new approach for pedestrian lane detection in unstructured environments, where the pedestrian lanes can have arbitrary surfaces with no painted markers. In this approach, a hybrid deep learning-Gaussian process (DL-GP) network is proposed to segment a scene image into lane and background regions. The network combines a compact convolutional encoder-decoder net and a powerful nonparametric hierarchical GP classifier. The resulting network with a smaller number of trainable parameters helps mitigate the overfitting problem while maintaining the modeling power. In addition to the segmentation output for each test image, the network also generates a map of uncertainty--a measure that is negatively correlated with the confidence level with which we can trust the segmentation. This measure is important for pedestrian lane-detection applications, since its prediction affects the safety of its users. We also introduce a new data set of 5000 images for training and evaluating the pedestrian lane-detection algorithms. This data set is expected to facilitate research in pedestrian lane detection, especially the application of DL in this area. Evaluated on this data set, the proposed network shows significant performance improvements compared with several existing methods.

中文翻译：

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混合深度学习-高斯过程网络用于非结构化场景中行人车道的检测。

在许多辅助和自主导航系统中，行人车道检测是一项重要任务。本文提出了一种在非结构化环境中行人车道检测的新方法，在该环境中，行人车道可以具有任意表面，而没有绘制标记。在这种方法中，提出了一种混合式深度学习-高斯过程（DL-GP）网络，用于将场景图像分割为车道和背景区域。该网络结合了紧凑的卷积编码器-解码器网络和强大的非参数分层GP分类器。得到的具有较少数量可训练参数的网络有助于减轻过拟合问题，同时保持建模能力。除了每个测试图像的分段输出外，网络还会生成不确定性图-该度量与我们可以信任细分的置信度水平呈负相关。该措施对于行人车道检测应用非常重要，因为其预测会影响其用户的安全。我们还引入了一个包含5000张图像的新数据集，用于训练和评估行人专用车道检测算法。该数据集有望促进行人车道检测的研究，尤其是DL在该领域的应用。根据该数据集进行评估，与现有的几种方法相比，拟议的网络显示出显着的性能改进。我们还引入了一个包含5000张图像的新数据集，用于训练和评估行人专用车道检测算法。该数据集有望促进行人车道检测的研究，尤其是DL在该领域的应用。根据该数据集进行评估，与现有的几种方法相比，拟议的网络显示出显着的性能改进。我们还引入了一个包含5000张图像的新数据集，用于训练和评估行人专用车道检测算法。该数据集有望促进行人车道检测的研究，尤其是DL在该领域的应用。根据该数据集进行评估，与现有的几种方法相比，拟议的网络显示出显着的性能改进。