Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (2): 719-729.doi: 10.13229/j.cnki.jdxbgxb20181043

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Indoor pedestrian detection and tracking from distributed two⁃dimensional laser range finders

Zhao-zheng HU1,2(),Zhao-kang LI1,Qian-wen TAO2   

  1. 1.School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China
    2.Intelligent Transportation System Research Center, Wuhan University of Technology, Wuhan 430063, China
  • Received:2018-10-18 Online:2020-03-01 Published:2020-03-08

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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

CLC Number: 

  • TP277

Fig.1

Flow chart of proposed method"

Fig.2

Virtual dihedron obtained from a plane calibration plate placed in two different position"

Fig.3

Pedestrian motion model based on step and pose model"

Fig.4

Two types of laser range finder for experiments"

Fig.5

Distribution of multiple measurement result of laser range finder at different angles"

Fig.6

Setup of LRF calibration with plane pattern (experiment 1)"

Fig.7

Diagram of surveillance scene"

Fig.8

Background model of monitoring scene after LGM (experiment 1)"

Fig.9

Target detection results based on DBSCAN algorithm cluster analysis (experiment 1)"

"

Fig.11

Setup of LRF calibration with plane pattern (experiment 2)"

Fig.12

Diagram of surveillance scene (experiment 2)"

Fig.13

Background model of monitoring scene after LGM (experiment 2)"

Fig.14

Target detection results based on DBSCAN algorithm cluster analysis (experiment 2)"

Fig.15

Kalman tracking results based on pedestrian step and pose model (experiment 2)"

Fig.16

Diagram of experience scene(experiment 3)"

Table 1

Comparison of results"

项目视场角/(°)识别率/%
白天傍晚晚上
HOG+SVM71.0591.789.0-
Mask R-CNN71.0598.996.2-
本文方法360.0098.598.798.6
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