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

Previous Articles    

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

RICH HTML

 5   

PDF (PC)

217

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
1 Uenoyama N, Niitsuma M. Temporal segmentation of environmental map based on changing rate of activity areas and improvement of tracking method using LRF[C]∥IEEE/SICE International Symposium on System Integration. Nagoya, Japan: IEEE, 2016: 57-62.
2 Seow Y, Miyagusuku R, Yamashita A, et al. Detecting and solving the kidnapped robot problem using laser range finder and wifi signal[C]∥IEEE International Conference on Real-time Computing & Robotics, Okinawa, Japan, 2018: 18-22.
3 张晓冬, 李建桥, 王洋, 等. 基于激光测距仪的地面不平度的测量与分析[J]. 吉林大学学报: 工学版, 2013, 43(5): 1258-1263.
Zhang Xiao-dong, Li Jian-qiao, Wang Yang, et al. Measurement and analysis of terrain roughness using distance laser sensor[J]. Journal of Jilin University (Engineering and Technology Edition), 2013, 43(5): 1258-1263.
4 赵静荣, 米阳, 张淑梅, 等. 人眼安全激光测距系统误差分析[J]. 吉林大学学报: 工学版, 2012, 42(增刊1): 410-414.
Zhao Jing-rong, Mi Yang, Zhang Shu-mei, et al. Error analysis of eye-safe laser rangefinder[J]. Journal of Jilin University (Engineering and Technology Edition), 2012, 42(Sup.1): 410-414.
5 Fernandez-Moral E, Gonzalez-Jimenez J, Arevalo V. Extrinsic calibration of 2D laser rangefinders from perpendicular plane observations[J]. The International Journal of Robotics Research, 2015, 34(11): 1401-1417
6 罗赞丰. 基于多激光的行人目标跟踪[D]. 浙江： 浙江大学信息与电子工程学系, 2012.
Luo Zan-feng. Pedestrian tracking based on multiple laser scanners[D]. Zhejiang: School of Information and Electronic Engineering, Zhejiang University, 2012.
7 Hu Z Z, Li Y C, Li N, et al. Extrinsic calibration of 2-D laser rangefinder and camera from single shot based on minimal solution[J]. IEEE Transactions on Instrumentation and Measurement, 2016, 65(4): 915-929.
8 Dylan F G, Takahiro M, Hiroshi I. Automatic position calibration and sensor displacement detection for network of laser range finders for human tracking[C]∥IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, 2010: 2938-2945.
9 Zhao H, Shibasaki R. A novel system for tracking pedestrians using multiple single-row laser-range scanners[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2005, 35(2): 283-291.
10 上海思岚科技有限公司. RPLIDAR A1低成本360度激光扫描测距雷达[EB/OL]. [2016-07-04].
11 北洋电机株式会社. Distance Data Output/UXM-30LXH-EWA[EB/OL]. [2018-10-18].
12 Ramsey F. Understanding the basic of the kalman filter via a simple and intuitive derivation[J]. IEEE Signal Processing Magazine, 2012, 29(5): 128-132.
13 Martin E, Hans-Peter K, Joerg S, et al. A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise[C]∥International Conference on Knowledge Discovery & Data Mining, Portland, Oregon， 1996: 226-231.
14 Golub G H, Van-Loan C F. Matrix Computation[M]. Maryland: Baltimore, Maryland: John Hopkins University Press, 1996.
15 Madsen K, Nielsen H B, Tingleff O. Methods for Non-linear Least Squares Problems Informatics and Mathematical Modeling[M]. Lyngby: Technical University of Denmark, DTU, 1999.
16 He K, Gkioxari G, Dollar P, et al. Mask R-CNN[C]∥2017 IEEE International Conference on Computer Vision(ICCV), Venice, Italy, 2017: 2980-2988.
17 Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]∥2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, USA, 2005: 886-893.
[1] Peng ZHAO,Yu-zhong JIANG,Bin CHEN,Chun-teng LI,Yang-yong ZHANG. SLF channel noise suppression method based on adaptive blanking in local variance domain [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1696-1705.
[2] LI Zhi-hui, HU Yong-li, ZHAO Yong-hua, MA Jia-lei, LI Hai-tao, ZHONG Tao, YANG Shao-hui. Locating moving pedestrian from running vehicle [J]. 吉林大学学报(工学版), 2018, 48(3): 694-703.
[3] QU Zhao-wei, WEI Fu-lu, WEI Wei, LI Zhi-hui, HU Hong-yu. Pedestrian detection by radar vision data fusion [J]. 吉林大学学报(工学版), 2013, 43(05): 1230-1234.
[4] LI Juan,SHAO Chun-fu,YANG Li-ya. Pedestrian detection based on improved Gaussian mixture model [J]. 吉林大学学报(工学版), 2011, 41(01): 41-0045.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd