People detection and distinction of their walking aids in 2D laser range data based on generic distance-invariant features

Christoph Weinrich, Tim Wengefeld, Christof Schröter, H. Groß
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引用次数: 39

Abstract

People detection in 2D laser range data is a popular cue for person tracking in mobile robotics. Many approaches are designed to detect pairs of legs. These approaches perform well in many public environments. However, we are working on an assistance robot for stroke patients in a rehabilitation center, where most of the people need walking aids. These tools occlude or touch the legs of the patients. Thereby, approaches based on pure leg detection fail. The essential contribution of this paper are generic distance-invariant range scan features for people detection in 2D laser range data and the distinction of their walking aids. With these features we trained classifiers for detecting people without walking aids (or with crutches), people with walkers, and people in wheelchairs. Using this approach for people detection, we achieve an F1 score of 0.99 for people with and without walking aids, and 86% of detections are classified correctly regarding their walking aid. For comparison, using state-of-the-art features of Arras et al. on the same data results in an F1 score of 0.86 and 57% correct discrimination of walking aids. The proposed detection algorithm takes around 2.5% of the resources of a 2.8 GHz CPU core to process 270° laser range data at an update rate of 10 Hz.
基于通用距离不变特征的二维激光距离数据中人的检测与助行器的区分
在移动机器人中,基于二维激光距离数据的人员检测是一种常用的人员跟踪线索。许多方法被设计用来检测成对的腿。这些方法在许多公共环境中表现良好。然而,我们正在为康复中心的中风患者研制辅助机器人,那里的大多数人都需要辅助行走。这些工具闭塞或触摸病人的腿。因此,基于纯腿部检测的方法是失败的。本文的主要贡献是二维激光距离数据中人体检测的通用距离不变距离扫描特征及其助行器的区分。有了这些特征,我们训练分类器来检测没有助行器(或拄拐杖)的人、带助行器的人和坐轮椅的人。使用这种方法进行人员检测,我们在有助行器和没有助行器的人员中获得了0.99的F1分数,86%的检测对其助行器进行了正确分类。相比之下,在相同的数据上使用Arras等人最先进的特征,F1得分为0.86,对助行器的正确识别率为57%。所提出的检测算法以10 Hz的更新速率处理270°激光距离数据,占用2.8 GHz CPU核心约2.5%的资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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