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首页> 外文期刊>International Journal of Advanced Robotic Systems >People detection and tracking using RGB-D cameras for mobile robots
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People detection and tracking using RGB-D cameras for mobile robots

机译：人们使用RGB-D摄像机进行检测和跟踪移动机器人

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摘要

People detection and tracking is an essential capability for mobile robots in order to achieve natural human-robot interaction. In this article, a human detection and tracking system is designed and validated for mobile robots using color data with depth information RGB-depth (RGB-D) cameras. The whole framework is composed of human detection, tracking and re-identification. Firstly, ground points and ceiling planes are removed to reduce computation effort. A prior-knowledge guided random sample consensus fitting algorithm is used to detect the ground plane and ceiling points. All left points are projected onto the ground plane and subclusters are segmented for candidate detection. Meanshift clustering with an Epanechnikov kernel is conducted to partition different points into subclusters. We propose the new idea of spatial region of interest plan view maps which are employed to identify human candidates from point cloud subclusters. Here, a depthweighted histogram is extracted online to feature a human candidate. Then, a particle filter algorithm is adopted to track the human's motion. The integration of the depth-weighted histogram and particle filter provides a precise tool to track the motion of human objects. Finally, data association is set up to re-identify humans who are tracked. Extensive experiments are conducted to demonstrate the effectiveness and robustness of our human detection and tracking system.
机译：人们检测和跟踪是移动机器人的基本能力，以实现自然人机器人互动。在本文中，使用具有深度信息RGB-Depth（RGB-D）摄像机的颜色数据为移动机器人设计和验证了人机检测和跟踪系统。整个框架由人类检测，跟踪和重新识别组成。首先，移除地点和天花板飞机以减少计算工作。先验知识的导向随机样本共识拟合算法用于检测接地平面和天花板点。将所有左点投射到接地平面上，并且分段为候选检测。 vinuShift群集与EPAnechnikov内核进行分区不同的点。我们提出了兴趣平面图的空间区域的新思想，这些地图是从点云亚运员识别人类候选人的地图。这里，在线提取深度重量的直方图，以特征为人类候选者。然后，采用粒子滤波器算法跟踪人类的运动。深度加权直方图和粒子过滤器的集成提供了一种追踪人体运动的精确工具。最后，设置数据关联以重新识别被跟踪的人类。进行了广泛的实验，以证明我们人类检测和跟踪系统的有效性和稳健性。

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