基于分布式二维激光测距仪的室内行人检测与跟踪

doi:10.13229/j.cnki.jdxbgxb20181043

摘要/Abstract

摘要：

针对多台二维激光测距仪的空间标定问题进行研究，提出了基于虚拟二面体的激光测距仪标定新方法，并将不同激光测距仪采集的激光数据映射至同一参考坐标系中。在此基础上，提出了激光高斯背景模型（LGM）对激光数据进行背景建模，检测出运动目标，然后通过DBSCAN算法对同一类的点云目标进行聚类分析。最后，通过行人运动模型并结合卡尔曼滤波算法实现行人在不同激光视场下的准确跟踪。在实验中，利用真实的室内场景对本文算法进行验证，并就视场角和不同光照条件下的识别率与现有算法进行对比。实验利用不同类型的二维激光测距仪对场景中的行人目标进行检测与跟踪。实验结果表明：利用多台分布式激光测距仪可在不受光照条件的影响下实现大视角的室内场景监控，利用本文算法能够从多台激光融合的点云数据中较精确且稳定地检测并跟踪行人目标。

关键词: 通信与信息系统, 室内监控, 二维激光测距仪标定, 激光高斯背景模型, 行人检测

Abstract:

This paper addresses the detection and tracking of pedestrians in indoor environments by using distributed Two-dimensional Laser Range Finders (2D-LRFs). We first proposed a novel algorithm to calibrate multiple 2D-LRFs based on a virtual dihedron such that all the 2D laser data from different 2D-LRFs can be mapped into a reference coordinate system. Then, based on the integrated 2D laser data, we proposed Laser Gaussian Model (LGM) to model the background and the Density-based Spatial Clustering of Applications with Noise (DBSCAN) for pedestrian detection. Finally, the detected pedestrians were tracked across different views of the multiple LRFs by Kalman filter. The proposed methods were tested and validated by using different 2D-LRFs in real indoor environments, and compared with existing algorithms in terms of the view angles and the recognition rate among different illumination conditions. The results demonstrate that the proposed distributed 2D-LRFs can monitor the indoor environments with very large view angles and unaffected by lighting conditions. The proposed methods can detect and track moving pedestrians accurately and reliably for indoor surveillance.

Key words: communication and information system, indoor surveillance, 2D LRF calibration, Laser Gaussian Model (LGM), pedestrian detection

中图分类号:

TP277

胡钊政,李招康,陶倩文. 基于分布式二维激光测距仪的室内行人检测与跟踪[J]. 吉林大学学报(工学版), 2020, 50(2): 719-729.

Zhao-zheng HU,Zhao-kang LI,Qian-wen TAO. Indoor pedestrian detection and tracking from distributed two⁃dimensional laser range finders[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 719-729.

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项目	视场角/(°)	识别率/%
项目	视场角/(°)	白天	傍晚	晚上
HOG+SVM	71.05	91.7	89.0	-
Mask R-CNN	71.05	98.9	96.2	-
本文方法	360.00	98.5	98.7	98.6

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