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Jia, S., et al.
Paper:
Human Recognition Using RFID Technology and Stereo Vision
Songmin Jia, Jinbuo Sheng, Daisuke Chugo, and Kunikatsu Takase
University of Electro-Communications
1-5-1 Chofugaoka, Chofu-City, Tokyo 182-8585, JapanE-mail: [email protected]
[Received November 1, 2007; accepted June 25, 2008]
In this paper, a method of human recognition in in-
door environment for mobile robot using RFID (Radio
Frequency Identification) technology and stereo vision
is proposed as it is inexpensive, flexible and easy to
use in practical environment. Because information of
human being can be written in ID tags, the proposed
method can detect the human easily and quickly com-
pared with the other methods. The proposed methodfirst calculates the probability where human with ID
tag exists using Bayes rule and determines the ROI for
stereo camera processing in order to get accurate posi-
tion and orientation of human. Hu moment invariants
was introduced to recognize the human being because
this method is insensitive to the variations in position,
size and orientation. The proposed method does not
need to process all image and easily gets some informa-
tion of obstacle such as size, color, thus decreases the
processing computation. This paper introduces the ar-
chitecture of the proposed method and presents some
experimental results.
Keywords: RFID, stereo vision, probability, mobile
robot, human detection
1. Introduction
Indoor environmental obstacle recognition of mobile
robot is main topic in order to navigate a mobile robot
to perform a service task at facilities or at home. S. Ikeda
and J. Miura developed 3D indoor environment modeling
by a mobile robot with omnidirectional stereo and LaserRange Finder [1]. Hiroshi Koyasu et al. developed om-
nidirectional stereo obstacle detection method for mobile
robot moves in dynamic environment [2]. D. Castro et
al. proposed LRF based obstacle detection [3]. T. Watan-
abe [4] developed moving obstacle recognition using opti-
cal flow pattern analysis for mobile robots. Robust recog-
nition of humans in images is important for many applica-
tions. Detection of human body is more complicated than
for objects as human body is highly articulated. Many
of the methods for human detection have been developed.
Papgeorgiou and Poggio [5] uses Haar-based representa-
tion combined with a polynomial SVM. The other leadingmethods uses a parts-based approach [6]. In this paper, a
novel method of indoor environmental human recognition
of mobile robot by using RFID (Radio Frequency Identi-
fication) system with a stereo camera is proposed as it is
inexpensive, flexible and easy to use in the practical en-
vironment. Because the information of human being can
be written in ID tags in advance, the proposed method en-
ables the obstacles recognition easily and quickly. In or-
der to localize the ID tags accurately, the Bayes rule wasintroduced to calculate probability where the ID tag exists
after the tag reader detects a tag. Then stereo camera was
started to processed the ROI (Region of Interest) deter-
mined by the results of Bayes rule. Because the proposed
method doesnt need to process all input image, and some
information of environment was got from ID tag, thus de-
creases the image processing computation, and enables to
detect the obstacles easily and quickly. Hu moment in-
variants, recognition method of visual patterns and char-
acters independent of position, size and orientation was
used. This paper introduces the architecture of the pro-
posed method and gives some experimental results.
The rest of the paper consists of 5 sections. Section 2
describes the structure of hardware of the proposed sys-
tem. Section 3 presents ID tag localization using Bayes
rule based on RFID technology. Section 4 details the prin-
ciple of the proposed method of human recognition. The
experimental results are given in Section 5. Section 6 con-
cludes the paper.
2. System Description
In our system, we developed a nonholonomic mobile
robot that was remodeled from a commercially availablemanual cart (Fig. 1 ). The structure of the front wheels
was changed with a lever balance structure to make mo-
bile robot move smoothly, and the motors were fixed to
the two front wheels. It has low cost and is easy to pass
over bump or gap between floor and rooms. We selected
Maxon EC motor and a digital server amplifier 4-Q-EC
50/5 which can be controlled via RS-232C [7]. For the
controller of mobile robot, a PC104 CPU module (PCM-
3350 Geode GX1-300 based) is used, on which RT-Linux
is running. For communication between mobile robot and
mobile robot control server running on the host computer,
the Wireless LAN (PCMCIA-WLI-L111) is used.KENWOOD S1000 series was used in the developed
28 Journal of Robotics and Mechatronics Vol.21 No.1, 2009
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8/3/2019 7966621
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Human Recognition Using RFID Technology and Stereo Vision
PC for Stereo VisionStereo Camera
RFID Reader
Robot
Controller
EC Motor
PC for Stereo VisionStereo Camera
RFID Reader
Robot
Controller
EC Motor
Fig. 1. The developed mobile robot platform.
system. Table 1 illustrates the specifications of KEN-
WOOD RFID system. Tag reader S1500/00 communi-cates with tags via 2.45 GHz radio wave. Since there is
a communication area between ID tag and tag reader (the
communication between mobile robot controller and tag
reader is via RS-232C), so if ID tag comes into the com-
munication area while mobile robot moves to the place
close to the ID tags, the ID tag can be detected and the in-
formation written in it can simultaneously be read out by
tag reader mounted on the mobile robot. When the work-
ing domain of mobile robot is changed or extended, what
needs to be done is just putting the new ID tags in new
environment and registering these ID tags to database. It
is also helpful to improve dynamic obstacles recognition
(such as chair or person).Bunmblebee (PGR, Point Grey Research) stereo cam-
era and MDCS2 (Videre Design) camera are usually used
in robotic field. In our system, we selected Bunmblebee to
integrate RFID technology to localize the service mobile
robot. The Bunmblebee two-camera Stereo Vision system
provides a balance between 3D data quality, processing
speed, size and price. The camera is ideal for applications
such as people tracking, mobile robotics and other com-
puter vision applications. Table 2 illustrates the specifi-
cations of Bunmblebee stereo camera. A note computer
(Intel Pentium3 M 1.00 GHz, Memory SDRAM 512 MB,
Windows XP Professional) was used to process input im-age.
3. Calculating ID Tag Existing Probability
Based on RFID Sensor Model
Obstacle Recognition is an important issue and a key
function for the mobile robot to perform a navigation-
based service task in indoor environment. We proposed
the method of indoor environmental obstacles with ID
tags recognition for mobile robot using RFID and stereo
vision. We introduce Bayes rule to calculate the probabil-
ity where the obstacle with ID tag, then recognize that the
obstacle is human or not.
Table 1. The specifications of KENWOOD RFID system.
SpecificationItem
Frequency 2.45GHz
Ca r d M emo r y s i z e 7 2 b y t e
The maximum
communication distance 4m
Interface RS -485,RS-232C
Power requirement DC24(V) 1 . 0(A)
W e i g h t ( r e a d e r ) 2 k g
Dimens ion (reader) 2 63x176x53mm (WxLxH)
SpecificationItem
Frequency 2.45GHz
Ca r d M emo r y s i z e 7 2 b y t e
The maximum
communication distance 4m
Interface RS -485,RS-232C
Power requirement DC24(V) 1 . 0(A)
W e i g h t ( r e a d e r ) 2 k g
Dimens ion (reader) 2 63x176x53mm (WxLxH)
Table 2. The specifications of Bunmblebee stereo camera.
Item Specification
Baseline 1 2 cm
Frame Rates 48 FPS (640x480)
Interfaces 6 -pin IEEE-1 394a
Power Consumption 2 .5W at12V
Dimensions 1 57 x 3 6 x 47. 4mm
Mass 3 42 grams
S ignal To Noise Ratio 60dB
Gain Automatic/Manual
Focal Lengths 6mm with 43
Item Specification
Baseline 1 2 cm
Frame Rates 48 FPS (640x480)
Interfaces 6 -pin IEEE-1 394a
Power Consumption 2 .5W at12V
Dimensions 1 57 x 3 6 x 47. 4mm
Mass 3 42 grams
S ignal To Noise Ratio 60dB
Gain Automatic/Manual
Focal Lengths 6mm with 43
3.1. RFID Probability Model
Obstacle recognition; specially, dynamic obstacle
recognition such as chair or human person is a difficult
problem. For human being, it is easy to avoid the obsta-
cles such as chairs, tables, but for mobile robot, it is very
difficult. We proposed the method of indoor environmen-tal obstacle recognition for mobile robot using RFID. Be-
cause the information of obstacle such as size, color can
be written in ID tags in advance, so the proposed method
enables the obstacle recognition easily and quickly. By
considering the probabilistic uncertainty of RFID, the
proposed method introduces Bayes rule to calculate prob-
ability where the obstacle exists when the RFID reader
detects a ID tag.
In our research, for the obstacle objects like chairs and
tables, we attached the ID tags on them, and the system
can detect them when the mobile robot moves the place
where ID tags enters the communication range of RFID
reader. Simultaneously, the data written in the ID tags can
also be read out. But localizing accurately the position of
Journal of Robotics and Mechatronics Vol.21 No.1, 2009 29