This study aims to develop a real-time algorithm, which can detect people even in arbitrary poses. To cover poor and changing light conditions, it does not rely on color information. The developed method is expected to run on computers with low computational resources so that it can be deployed on autonomous mobile robots.,The method is designed to have a people detection pipeline with a series of operations. Efficient point cloud processing steps with a novel head extraction operation provide possible head clusters in the scene. Classification of these clusters using support vector machines results in high speed and robust people detector.,The method is implemented on an autonomous mobile robot and results show that it can detect people with a frame rate of 28 Hz and equal error rate of 92 per cent. Also, in various non-standard poses, the detector is still able to classify people effectively.,The main limitation would be for point clouds similar to head shape causing false positives and disruptive accessories (like large hats) causing false negatives. Still, these can be overcome with sufficient training samples.,The method can be used in industrial and social mobile applications because of its robustness, low resource needs and low power consumption.,The paper introduces a novel and efficient technique to detect people in arbitrary poses, with poor light conditions and low computational resources. Solving all these problems in a single and lightweight method makes the study fulfill an important need for collaborative and autonomous mobile robots.

中文翻译：

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使用深度信息在任意姿势下进行快速头部检测

本研究旨在开发一种实时算法，即使在任意姿势下也能检测到人。为了覆盖较差和不断变化的光照条件，它不依赖于颜色信息。所开发的方法有望在计算资源较少的计算机上运行，​​以便可以部署在自主移动机器人上。该方法旨在拥有具有一系列操作的人员检测管道。具有新颖头部提取操作的高效点云处理步骤提供了场景中可能的头部簇。使用支持向量机对这些集群进行分类导致高速和鲁棒的人员检测器。该方法在自主移动机器人上实施，结果表明它可以检测到帧率为 28 Hz 且相等错误率为 92% 的人员. 还有，各种非标准姿势，检测器仍然能够有效地对人进行分类。主要限制是类似于头部形状的点云导致误报和破坏性配件（如大帽子）导致误报。尽管如此，这些可以通过足够的训练样本来克服。,该方法具有鲁棒性、低资源需求和低功耗，可用于工业和社会移动应用。本文介绍了一种新颖有效的技术来检测任意人姿势，光线条件差，计算资源低。以一种单一的轻量级方法解决所有这些问题，使这项研究满足了对协作和自主移动机器人的重要需求。主要限制是类似于头部形状的点云导致误报和破坏性配件（如大帽子）导致误报。尽管如此，这些可以通过足够的训练样本来克服。,该方法具有鲁棒性、低资源需求和低功耗，可用于工业和社会移动应用。本文介绍了一种新颖有效的技术来检测任意人姿势，光线条件差，计算资源低。以一种单一的轻量级方法解决所有这些问题，使这项研究满足了对协作和自主移动机器人的重要需求。主要限制是类似于头部形状的点云导致误报和破坏性配件（如大帽子）导致误报。尽管如此，这些可以通过足够的训练样本来克服。,该方法具有鲁棒性、低资源需求和低功耗，可用于工业和社会移动应用。本文介绍了一种新颖有效的技术来检测任意人姿势，光线条件差，计算资源低。以一种单一的轻量级方法解决所有这些问题，使这项研究满足了对协作和自主移动机器人的重要需求。该方法具有鲁棒性、低资源需求和低功耗等优点，可用于工业和社会移动应用。本文介绍了一种新颖有效的技术，可以检测光线条件差、计算资源低的任意姿势的人。以一种单一的轻量级方法解决所有这些问题，使这项研究满足了对协作和自主移动机器人的重要需求。该方法具有鲁棒性、低资源需求和低功耗等优点，可用于工业和社会移动应用。本文介绍了一种新颖高效的技术，可以检测光线条件差、计算资源低的任意姿势的人。以一种单一的轻量级方法解决所有这些问题，使这项研究满足了对协作和自主移动机器人的重要需求。