RGB-D People Dataset Dataset

In the RGB-D Peoples Dataset the authors present a 3D people detection and tracking approach using RGB-D data. It combines a novel multi-cue person detector for RGB-D data with an on-line detector that learns individual target models. The two detectors are integrated into a decisional framework with a multi-hypothesis tracker that controls on-line learning through a track interpretation feedback. For on-line learning, they take a boosting approach using three types of RGB-D features and a confidence maximization search in 3D space. The approach is general in that it neither relies on background learning nor a ground plane assumption.

在RGB-D People数据集中，作者们提出了一种基于RGB-D数据的3D人体检测与跟踪方法。该方法将一种新颖的多线索RGB-D人体检测器与一个在线检测器相结合，该在线检测器学习个体目标模型。这两个检测器被整合到一个决策框架中，其中包含一个多假设跟踪器，通过轨迹解释反馈来控制在线学习。对于在线学习，他们采用了一种基于三种类型的RGB-D特征和三维空间中置信度最大化搜索的增强方法。该方法是通用的，因为它既不依赖于背景学习，也不基于地面平面假设。

搜集汇总

背景与挑战

背景概述

RGB-D People Dataset是一个包含6798张RGB-D图像的数据集，主要用于监控和机器人领域的目标检测和深度估计任务。数据集包含4700个标注的‘人’类对象，31%的图像未标注，由三个垂直安装的Kinect传感器采集，适用于3D人物检测和跟踪研究。