论文标题
使用CNN检测RGB-D数据中的人
Detecting Humans in RGB-D Data with CNNs
论文作者
论文摘要
我们解决了RGB-D数据中人们检测的问题，在该数据中我们利用深度信息开发了一种利益区域（ROI）选择方法，该方法为两种颜色和深度CNN提供了建议。为了结合两个CNN产生的检测，我们根据深度图像的特征提出了一种新型的融合方法。我们还提出了一个新的深度编码方案，该方案不仅将深度图像编码为三个通道，而且还增强了分类信息。我们对公开可用的RGB-D人数据集进行了实验，并表明我们的方法的表现优于仅使用RGB数据的基线模型。
We address the problem of people detection in RGB-D data where we leverage depth information to develop a region-of-interest (ROI) selection method that provides proposals to two color and depth CNNs. To combine the detections produced by the two CNNs, we propose a novel fusion approach based on the characteristics of depth images. We also present a new depth-encoding scheme, which not only encodes depth images into three channels but also enhances the information for classification. We conduct experiments on a publicly available RGB-D people dataset and show that our approach outperforms the baseline models that only use RGB data.
