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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: jia@taka.is.uec.ac.jp

[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

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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