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International Journal of Information Engineering Sept. 2012, Vol. 2 Iss. 3, PP. 106-115
© American V-King Scientific Publishing - 106 -
Vehicle Accident Detection and Identification Using Image Compression Analysis and RFID Traffic
Cone Tracking System Module Songkran Kantawong
1, Tanasak Phanprasit
2
Department of Electronics and Telecommunication Engineering, Faculty of Engineering, Bangkok University
9/1 Moo 5 Phaholyothin Rd. Klongnuang, Klongluang, Pathumtani Thailand [email protected];
Abstract- This paper proposed an intelligent RFID traffic cone
that is applied for vehicle accident detection and identification
based on image compressing analysis and RFID detection
tracking in an accident clamming system and traffic reporting
system. The gain benefit of this paper can reduce the waiting time of an accident clamming process that usually used along
time with normally vehicle crash accessing and reporting
systems, especially those related to an image processing and
insurance techniques. This RFID technique deals with multi-
vehicles, multi lane and multi road even or traffic junction area. It provides an efficiency time management scheme with
enough correctly data reporting, in which a dynamic time
schedule is worked out in real time for the driver or passengers
of each accident situations. The accessing time operation of the
RFID traffic cone system emulates the judgment of a traffic policeman on duty or user who may have the PDA nearby the
RFID traffic cone that can be connected via by Wireless
channels. The image compression present here is used along
with the RFID information of each vehicle to get a precise event data picture that is composed of image encoding and
decoding algorithms called wavelet transform with Principle
Component Analysis (PCA) via Vector Quantization
techniques (VQ). The small bit rates for high-speed data
transmission with a small space for data storage area are required on wireless transmission channel. Simultaneously, the
peak signal to noise ratio (PSNR) has to be maintained. The
traffic management system model is constructed for testing this
present idea that composed of traffic lights, vehicles transit,
vehicles clash and traffic cone with RFID solution system. By applying the proposed technique, performance has been
improved which indicated by lower bit rate and better PSNR
for an image compression algorithm. The RFID traffic cone
mechanisms are work well while the RFID data tags that are
recorded of each vehicle are enough correctly and can be sent the data to the traffic information center or an accident
clamming center via on local area network (LAN) or wide area
network (WAN) simultaneously. For scaling the large number
of vehicles in a real situation, the simulation model is used to
test this system and reviewed that this proposed technique may be work well.
Keywords- RFID Traffic Cone; PCA; VQ; DWT; CLC;
CLC+SEC
I. INTRODUCTION
The RFID solutions in the fields of an intelligent traffic
management system [1-2]
started more recently but especially
increased rapidly in transit intelligent transportation system [3]
or an Automatic Vehicle Identificat ion (AVI) system, but
rarely see in the topic of vehicles clash or vehicles accident
clamming system. The objective of this paper is to present
the new idea o f usefully RFID traffic cone mechanism
designed with RFID solution algorithm [4-5]
which is
combined with an image compression analysis and RFID
tracking technique that are applied in vehicle accident
detection and identification. The gain benefits of this idea
are hoped to reduce the complexit ies of an accident claming
procedure that usually used a long time to make any
decisions among stakeholders such as drivers, claimers or
policeman. An image of each vehicle accident detection and
identification with RFID solutions can be sent to the
clamming center o r t raffic policeman station via on wireless
transceiver channels such as mobile phone or PDA as soon
as the RFID traffic cone station is installed in that area. The
main ideas of automatic vehicle detection with RFID
tracking system and system b lock d iagram are shown in
Figures 1 and 2 respectively.
Fig. 1 A sample of automatic vehicle detection with RFID tracking system
In the system block d iagram, the vehicle crash can be
detected by a s mall CCD camera that is installed with RFID
traffic cone station and evaluated with an image
compression algorithms for reducing the size of its data with
small b it rate and high PSNR for wireless transceiver
channel via on Pocket PC (PDA) connecting or may be store
in an embedded system in the next future work before
sending an appropriate image data together with vehicle
informat ion’s that are tracking by RFID traffic cone. The
RFID traffic cone can be easily operated by traffic
policeman and then the car crash informat ion is sent to an
accident clamming system and evaluated by RFID traffic
cone software and then store this informat ion in its database
and may be sent this report to the traffic management centre
in the same t ime. The remaining of this paper is organized
as follows. Sect ion II presents the new idea of RFID traffic
International Journal of Information Engineering Sept. 2012, Vol. 2 Iss. 3, PP. 106-115
© American V-King Scientific Publishing - 107 -
cone designed. Next section describes an image
compression analysis system composed of encoding process,
decoding process and code book designed with PCA
algorithms. Section IV provides the traffic system module
with RFID installation. Section V illustrates the
experimental results and finally Sect ion VI for conclusions.
I/ O
Module
Transceiver Module
RF-24GHz
VCRFID
Image Encoding
CCD
Camera
Code Book
Nearest Neighbor
Rules
1e1e
Image Decompression
(Vector Quantization)
Rx
Look Up Table
Code Book
Index
1 Level IDWT/PCA Decoder
1e
2e
Tx
Vehicle Accident Detection using RFID Traffic Cone System (VCRFID)
(Vector Quantization )
Index
1 Level PCA/ DWT
VCRFID
System
Internet
Antenna Reader
Police
Admin
users
Image Processing / RFID Traffic Cone System
I/ O
Module
Transceiver Module
RF-24GHz
VCRFID
Image Encoding
CCD
Camera
Code Book
Nearest Neighbor
Rules
1e1e1e1e
Image Decompression
(Vector Quantization)
Rx
Look Up Table
Code Book
Index
1 Level IDWT/PCA Decoder
1e1
e
2e2
e
Tx
Vehicle Accident Detection using RFID Traffic Cone System (VCRFID)
(Vector Quantization )
Index
1 Level PCA/ DWT
VCRFID
System
Internet
Antenna Reader
PolicePolice
AdminAdmin
usersusers
Image Processing / RFID Traffic Cone System
Fig. 2 System block diagram
II. TRAFFIC CONE ARCHITECTURE DESIGN
The basically commercial traffic cones are divided into
two types that are composed of hard traffic cones and elastic
traffic cone as shown in Figure 3. Hard traffic cones are normally used but can be broken easily and can’t be
reshaped, while reflect ive traffic cones can be more
effective in these problems but diff icult to use in practical
because of its need of power supply or any energy supply
connection or some technically step to use. So the types of
traffic cones are one of the key important factors to concern
about them effect ive to use in a real experiment. The main
advantages of both basically hard traffic cone and an elastic traffic cone are constructed in this RFID traffic module. It
can be dynamic shape change with an elastic th in sheets and
also unbreakable easily in the same time. The wireless
control method with RFID sensing module can take place in
any traffic area easily. The structure of RFID traffic cone is
mainly composed of plastic material with slide reflect ive
bars about 5 to 7 pieces that can be expand for 75-86 cm.
high and 38 cm. circular bases wide. The RFID tags, small
CCD camera, s mall dc motor and wire less transceiver module are installed on this model as shown in Figure 4.
Fig. 3 An examples of basically traffic cone in (a) hard traffic cone and (b)
elastic traffic cone
Fig. 4 The prototype model of RFID traffic cone mechanism design
III. IMAGE COMPRESSION SYSTEM
One issue of researches in an image compression system
is to find some coding methods with low bit rate and high
PSNR qualit ies in order to enhance the efficiency of rea l-
time image transmission. The Closed Loop Control (CLC)
plus System Error Compensate (SEC) with principle
component analysis (PCA) are analysed for an image
compression mentioned in this paper as described below.
A. Image Encoding and Decoding system
The encoding process is consists of two importance
steps. First, the Closed Loop Control (CLC) method is
reconstructed from the relat ionship between the codebook
and the nearest neighbours rule. Second, the 1-level DWT
with PCA encoder is applied to transform to the system
error in frequency domain to reduce the system error that
only the low frequency component is allowed to transmit to
International Journal of Information Engineering Sept. 2012, Vol. 2 Iss. 3, PP. 106-115
© American V-King Scientific Publishing - 108 -
decoding process with 1-level IDWT and PCA decoder as
shown in Figure 5.
ix
ix
ix
ox
11e
Encoder
Decoder
Index
CodebookNearest
Neighbor Rule
Lookup TableCodebookPCA
Decoder
PCA
Encoder
1 Level
DWT
1 Level
IDWT
2e
22e
33e
1eix
ix
ix
ox
11e
Encoder
Decoder
Index
CodebookNearest
Neighbor Rule
Lookup TableCodebookPCA
Decoder
PCA
Encoder
1 Level
DWT
1 Level
IDWT
2e
22e
33e
1e
Fig. 5 A diagram of system error add vector quantization system
[6]
The system error and system error compensate can be
calculated in (1). In order to cover for all frequency, wavelet
transforms (Haar) and the system error will be decomposed
by 1-level wavelet transform and only the low-low (LL)
sub-band will be trans mitted while other low-high (LH),
high-low (HL) and high-high (HH) sub-bands are discarded.
1 i ie X X ,
1
2 1( )e T e (1)
2o iX X e ,
)()( 1
1
10 eTeXX i
(2)
Where 1e is system error,
2e is system error
compensate, 1T
is inverse transform, iX is input image,
iX is reconstructed image and oX is output image.
Therefore, if system error compensates are approached
system error as close as possible the output image will
approach input image and perform a low bit-rate and high
PSNR simultaneous in (2).
B. Close Loop Control System (CLC)
The 256x256 input pixels are divided into 4,096 p ixels
of 4x4 square sub blocks which is called as input vector.
The Nearest Neighbour Rule is an error retrieved from the
calculation of mean square error (MSE) between input
vector and index code vector that kept in the codebook.
21( , ) [ ( ) ( )]
K
i i i i
m
d X X X m X mK
(3)
Where ( , )i id X X is mean square error, ( )iX m is input
vector, ( )iX m is code vector and K is dimension of vector.
C. System Error Compensate System (SEC)
Compression methods in vector quantization style are
basically caused information loss and may be occurred
blocky effect due to system error with none compensated.
The system error compensation (e1) is consisting of two
main processes. In the first process, system error is
calculated from input image of size 256x256 p ixels and the
reconstruction image. In this paper, the
Principle Component Analysis (PCA) [7]
is applied to
reduced the dimension (4x16,384 pixels) of system error,
called System Error1(e11). Second, System Error1 is
decomposed into 2 sub-bands [LL1 (1x8,192 pixels), HH1
(1x8,192 p ixels)] to perform 1-level discrete wavelet
transform (DWT) of system error by Haar technique.
However, only LL1 sub-band is used, so the following
methods are calculated step by step as below.
Step 1: Get some data )( i
1
2
4096
:i
(4)
Step 2: Subtract the mean( )i
1
1;( 1,2,...,4,096: 1,2,...,16)
M
i i
n
i MM
(5)
i i i (6)
Step 3: Calcu late the covariance matrix (C)
1 2 4,096,...,A (7)
C =ATA (8)
Step 4: Calcu late the eigenvectors and eigen values of
the covariance Matrix
Step 5: Choosing components and forming a feature
vector
Step 6: Deriv ing the new data set
D. Combination of CLC and SEC System
The combination of CLC and SEC consists of three parts.
First, input of decoding processes are consists of index and
system error. Part II are consists of look up table, codebook
and inverse discrete wavelet transform (IDWT) process.
Second, System Error2 (e33) in LL1 sub-band is composed
by two sub-bands called LL1 and HH1. However, before
done the HH1 composition it must be set to zero. The all two
sub-bands will be decomposed by IDWT in order to
construct system error (e22) compensate. After that system
error1 is decoded and will be composed further more into
system error compensate (e2). Finally, the reconstructed
image and system error compensation will be combined in
order to reconstruct an output image that have error less,
low b it rate and high PSNR.
International Journal of Information Engineering Sept. 2012, Vol. 2 Iss. 3, PP. 106-115
© American V-King Scientific Publishing - 109 -
IV. TRAFFIC SYSTEM MODULE AND RFID TRAFFIC CONE
INSTALLATION
A. RFID Tra ffic Cone Mechanism Design and Installation
The proposed of RFID traffic cone with RFID reader,
battery backup and infrared transceiver circuit board
installation are designed here. The vertical metal alloy rod
point at the center position of the conic mechanis m based
was droved by small DC motor for expanded the elastic thin
sheets in upward d irection and can be reshaped in
downward position simultaneously. This model can be
easily used when push on the remote control key and also
easily kept when push off key. An advantage of wireless
controlling signal is that it is able to take place this traffic
cone anyway of any traffic system area as shown in Figures
6 to 8 respectively.
Fig. 6 A traffic cone with vertical rod controlled by small dc motor and
infrared transceiver circuit board
Fig. 7 An elastic thin sheets of RFID traffic cone mechanism design
Fig. 8 A complete model of RFID traffic cone mechanism design
The RFID Mifare Read/Write Module SL015M-1 was
selected to use for high frequency range about 13.56 MHz,
UART interface, baud rate about 9,600-115,200 bps depend
on protocol ISO 14443A (Mifare) that supporting for Tag
Mifare 1Kbyte, Mifare 4Kbyte, Mifare Ultra Light with
built in antenna. By using passive RFID tags, the
identification range can reach 80 mm along with 0.5 m/sec
speed and certain models are susceptible to the moisture and
ambient temperature )70~20( CC in operating process.
Fig. 9 The RFID mifare read/ write module with ID tag
An intelligent traffic cone described here takes an
advantage of RFID system [8-9]
that non-contact data
communicat ion is possible which can read and write data on
a tag via radio waves or electromagnetic waves. It consists
of a tag (data carrier, ID card) with data store which having
a capacity enough to record more information than
identification codes, an antenna which communicates with
the tag, a controller which controls the antenna, higher-level
equipment (system) which controls the controller and small
size enough to carry around or to use by attaching on an
object. The tag can be read even if the position or angle of
the tag and antenna is not proper. The data signal from
RFID tag can be read by RFID reader with enough
efficiency media channel and not obstacle signal in line of
sigh even if they are passed by air, water, p lastic, mirror or
other thick materials.
Fig. 10 RFID tag sensing area
B. RFID Tag with Road Sensing Detection Technique
The proximity sensors are installed along distance about
34 cm between active sensing area for detecting and speed
calculating of the vehicle modelling quantities that passed
through to the traffic lights system at the cross road
intersection as shown in Figure 11 [10-11]
.
Fig. 11 The road sensing detection area
C. Pedestrian Sensing Detection Technique
By using RFID for prevention of pedestrian accident [12]
situation, the active sensing areas are detected by proximity
sensors that installed near the pedestrian tower light at the
both side of the road and operate synchronizing with traffic
light timing function system when crosswalk users are in
this area.
Fig. 12 Pedestrian sensing area installation
International Journal of Information Engineering Sept. 2012, Vol. 2 Iss. 3, PP. 106-115
© American V-King Scientific Publishing - 110 -
D. RFID Tra ffic Cone Installation System
The traffic system model is composed of road ways,
traffic lights, pedestrian lights, vehicle sensing modules and
RFID reader station as shown in Figures 11 to 14. The RFID
tags were installed on vehicles that transit on the conveyor
belt droved by DC motor, while RFID reader/ writer run on
traffic cone in detection area and can be done even if the
vehicle module was fixed position or moved not quickly.
Fig. 13 Vehicle detection by RFID traffic cone module
The vehicle models with RFID tags can be detected and
identified when its pass to the RFID t raffic cone station that
is located in traffic system model [13-14]
via on wire line or
Wi-Fi channel to client computer with RFID t raffic cone
software evaluation as shown in Figure 14.
Fig. 14 The RFID traffic cone station with vehicle detection module
The essential data that came from these RFID tags of
vehicle models are composed of reading number, car ID,
location of RFID record station and time of record as shown
in Figure 15.
When the client computer finishes this essential data tag
recorded it can send the data to the main server via on LAN
or WLAN network. The server is responsible evaluated for
managing the data of all client computers that are attempt to
access to the server in media access control (MAC) channel
that are composed of all reading number, all car ID, all
location of RFID traffic cone record stations, all name of
vehicle’s author and time of records as shown in Figures 16
to 17.
Fig. 15 An example of information came from each of RFID traffic cone
with ID tag record in client data based
Fig. 16 An example of information came from each of RFID traffic cone
with ID tag record in server data based
Fig. 17 An example of summary information came from each of RFID
traffic cone with ID tag record in server data based
The RFID traffic cone statistical report from this server
system process will monitor and store all of traffic data
system that relat ively with all RFID traffic cones that are
installed in each traffic system area and can be printed out
automatically for graph representation as shown in Figure
18.
Fig. 18 An example report of RFID traffic cone with statistical of traffic
system module
E. RFID Tra ffic Cone Software Development
The RFID traffic cone software present here is divided
into mainly three parts, first for admin istrator that can
monitor and implement all of system. Second part for d river
is their owner vehicles tag or car tag for registration in this
system and finally for other user or police man can do only
the informat ion report. Each RFID tag which unique ID
code worldwide contains car informat ion, car status and
other informat ion’s of car owner. The ID code and its
International Journal of Information Engineering Sept. 2012, Vol. 2 Iss. 3, PP. 106-115
© American V-King Scientific Publishing - 111 -
informat ion are stored at the informat ion exchange networks
of the public security department. This code is kept in static
code style and will alert when the car is stilled in an
accident situations or any traffic informat ion is inquired by
the car owner.
The data based on all system are designed by Microsoft
SQL Server 2005 program controlled by two mainly units
that first for server program and second for client program.
The relation of data based categories is shown in Figure 20.
Fig. 19 The RFID traffic cone software development with USB 2 serial
ports communication
Fig. 20 RFID traffic cone data based design
An admin istrator can login to the system and manage for
all of sub programs that are representative as the
communicat ion part and data management part in Figures
21, 22.
Fig. 21 Login window page form of RFID traffic cone design with
administrator program
The ID tags and RFID traffic cones are connected in
communicat ion program and then sent this data to process
and store in database system. The data management
programs are d ivided into four parts that are composed of
Reader Manage program, Register program, Fu ll v iew
program and Statistical program. The RFID reader
informat ion is managed by Reader Manage program for
registration, data correction, data erasion and RFID reader
configuration. While the reg istration program is contained
of car ID tag that can be store, change, delete or renew this
data efficiency. The output of registration program is shown
in Figure 23 that the ID tag must be installed firmly, reliab ly
and as possible as concealed. It can be installed in special
manner so that it can alarm automat ically once be taken
down, without being misused.
Fig. 22 Main window page form of RFID traffic cone program
Fig. 23 Registration layer page form of RFID traffic cone program
For review or display of all system data can be done by
the full view diagram as shown in Figure 24 that is
representative for the working flow diagram designed. The
working flow diagram is composed of five parts namely
reading, car, human, reader and SQL command respectively.
Start
Full View
Window
Reading ReaderHumanCar
Display Display Display Display
SQL Command
Display
END Fig. 24 The working flow diagram designed of RFID traffic cone program
International Journal of Information Engineering Sept. 2012, Vol. 2 Iss. 3, PP. 106-115
© American V-King Scientific Publishing - 112 -
For the client part, user can login to the system with
their user password or user ID which contains a sequence of
entries and after that the window form of RFID data based is
uploaded and prepared to connect to RFID reader for
receiving RFID tags automatically. These entries can be
divided into three parts. First is for opening service form for
RFID reader connecting with RFID tags. Second is for ID
tag information display and ID tag analysed. Finally, for
RFID t raffic cone statistical reports that this process will
monitor and store all of traffic data system that relatively
with traffic cone and this code can help the police man that
used the intelligent traffic cone to identify and track the
accident car.
Fig. 25 The window page form designed of RFID traffic cone program with
graphic user interface (GUI)
Movable traffic cone station is usually applied in the
traffic system module for detect the vehicles of vital
communicat ion lines or wireless with PDA to examine,
identify and record the passing vehicles. Equipments such as
reader/writer, intelligent traffic cone controller, data
transmission unit and power supply are installed in the
traffic system model at under or beside the road. While there
is a car with ID tag passing through the line, the system can
read the ID code of the tag, time for passing and the line
number and then store this information into the controller’s
memory. The data transmission unit can pass the vehicle
data informat ion collected to personal computer or public
security department or traffic administration centre via
networks (data based), meanwhile communicate the
command of admin istration centre to the intelligent traffic
cone system to depend whether the car can pass through
normally or not in the situation of their crash or has an
accident in traffic area.
V. EXPERIMENTAL RESULTS
For evaluating the performance of presented image
coding method, it has been produced by specification
Pentium(R) 4 CPU 3.01 GHz; b lock size 4x4, codebook size
is 256 and an image size equal to 256x256 p ixels. The bit
rate is computed as 0.50bpp [((256*256*8)/ (4*4))/
(256*256) = 0.50bpp] and the file size is 256*256*0.50 =
32 Kbytes.
TABLE I A RECONSTRUCTION IMAGE OF VEHICLE ACCIDENT DETECTION
BY IMAGE COMPRESSION ALGORITHMS WITH EQUAL BIT RATE AT 0.5BPS
Vehicle Accident Image (Room laboratory results)
CLC Compression
CLC+SEC Compression
PSNR (dB)
CLC CLC+SEC
24.27 28.12
26.66 30.91
22.38 26.83
28.25 33.72
25.58 29.32
26.20 32.00
23.51 28.88
26.25 31.55
25.56 30.85
Experimental results of vehicle accident images
compression reveal that the CLC+SEC algorithm gave
superior performance than CLC algorithm of all image
pictures that evaluated as 0.50bpp of high PSNR. So the
CLC+SEC method is selected to send this image
compression to server. The experimental results of vehicle
models detection in traffic system model are shown in
Figures 26, 27 and Table II.
Fig. 26 The testing result of RFID traffic cone system program
International Journal of Information Engineering Sept. 2012, Vol. 2 Iss. 3, PP. 106-115
© American V-King Scientific Publishing - 113 -
Fig. 27 The testing result of RFID traffic cone system program
From Figure 27, the average speed of vehicle models is
equal to sec/398.0 m for ten time experiments .
TABLE II EXPERIMENTAL RESULTS OF VEHICLE MODULE DETECTION (TEN
DIFFERENT TRANSIT ROUND PER EACH CAR MODELS AT AVERAGE SPEED OF
TRANSIT BELT EQUAL TO sm /398.0 )
Image Vehicle Model
Correct Results
ID Tags (Hex) Percent of Correction
พร 9988 4
BE C3 39 39 38 B5 C3 FF FF FF FF FF FF FF
80
กม 1122 5 A1 C1 31 31 32 32 C0
A1 FF FF FF FF FF FF 100
วจ 7879 5 CE C4 40 40 37 37 C4
E0 FF FF FF FF FF FF 100
ศส 1234 5 EE C5 41 41 39 35 C5
FF FF FF FF FF FF FF 100
ฟห 2562 5 CE C1 32 32 33 33 C2
FF FF FF FF FF FF FF 100
กข 2213 4 DD D1 33 33 35 35 C0
FF FF FF FF FF FF FF 80
ทม 6798 5 AE A1 22 22 28 29 A0
FF FF FF FF FF FF FF 100
จช 791 5 BB C5 41 41 49 49 B1
FF FF FF FF FF FF FF 100
ดต 251 4 ED E3 30 30 37 37 C0
FF FF FF FF FF FF FF 80
ชพ 791 5 AE C0 35 35 38 B5 C8
FF FF FF FF FF FF FF 100
Average total correct detection (%) 94.00
The RFID informat ion of each vehicles were composed
of car owners, licence, car registration numbers, car types,
colours, date and time of record, registration places, area of
accident events, RFID tags and the other data that may be
usefully for vehicle accident detection and identification by
RFID traffic cone solution system.
Fig. 28 The testing result of RFID traffic cone system program
For scale up this vehicles detection results to an actual
traffic system for an estimating the enough vehicles of
traffic characteristic response per one RFID t raffic cone
installation in the terms of capacity and congestion situation
in traffic network, the simulation results of clients that are
attempt to access to the server unit both for LAN and
WLAN are done by computer notebook CPU Centrino II,
RAM 2GByte, 2.16 GHz with RFID traffic cone that are
shown in Table III and Figure 29.
TABLE III EXPERIMENTAL RESULTS OF VEHICLE MODULE DETECTION
(CLIENT TO SERVER NETWORK)
Clients Number of receiving data per second in
server networks
Number of users
(Vehicles) LAN WLAN
5 47 45
10 90 85
15 119 115
20 136 125
25 122 116
30 120 80
Users
Nu
mb
er
of
rece
ive
rs/s
eco
nd
Simulation of RFID traffic cone data sending from client to server networks
Users
Nu
mb
er
of
rece
ive
rs/s
eco
nd
Simulation of RFID traffic cone data sending from client to server networks
Fig. 29 The simulation results of vehicle detection by an intelligent RFID traffic cone software via on server network connection
The simulation results show that the local area network
(LAN) server is highly traffic capacity than wireless local
area network (W LAN) for all of number of users in an
International Journal of Information Engineering Sept. 2012, Vol. 2 Iss. 3, PP. 106-115
© American V-King Scientific Publishing - 114 -
effective of data collision of media access channel
contention (MAC) protocol. The suitable number of users or
throughput that are attempted to access to the server per one
RFID t raffic cone receiver that can gain an effective
response for the maximum capacity of server network both
for LAN and WLAN is about of 20 users. For more than 20
users the collisions of accessing requests will be more
effective in the media contention resolution of MAC
channel that can drop the throughput down. For testing the
traffic cone mechanism design, the PIC12F675
microcontroller is used to control the system that is received
the remote command by infrared transceiver module for
controlling the 12 VDC motor both in upward and
downward direction of traffic cone at average speed of
0.136 m/s. The d istance range of remote command may be
in 3 to 5 meters with not obstacle objects in line of sight.
User or po liceman with their remote control can be remote
this RFID traffic cone for more convenience to use and in
the case of losing remote control, th is RFID traffic cone can
be done by manual control easily.
Fig. 30 The testing result of RFID traffic cone mechanism operation
Fig. 31 The testing result of vehicle tracking by RFID traffic cone in real environmental
VI. CONCLUSIONS
In this paper, we present an intelligent RFID traffic cone
system module that can be applied for vehicle accident
detection and identification method in an accident clamming
system. The information of each car clash can be read and
stored in RFID traffic cone software and then sent the data
to the traffic admin istration centre via on communication
networks both for wire line or wireless channel or internet
network. The room experimental results reveal that the
CLC+SEC method of image compression algorithms gave
superior performance than CLC method that evaluated as
0.50bpp and high PSNR respectively. The RFID traffic cone
mechanis m can be remote controlled by user easily both in
upward and downward direction by small DC motor while
the RFID traffic cone software can be evaluated with h ighly
average correction results in many example of different car
tags and store these data in database and then can be send
this vehicle information of each accident situation via on
communicat ion network channels or print out by hard copy.
The traffic system model in a laboratory room revealed that
the RFID t raffic cone module with RFID reader can be read
enough correctly data for more than 94 percentage of ten
different vehicle models that are in general more effect ive
than a real environmental t raffic system area. The suitable
users that can gain the maximum throughput of network
capacity per one RFID traffic cone are about 20 users. This
means that the performance of image compression method
and the range of detection distance of RFID traffic cone
reader must be improved for more efficiency such as more
than 10 meters in the microwave frequency range that can
be installed easily in wherever of traffic target area.
ACKNOWLEDGMENT
Department of Electronics and Telecommunicat ion
Engineering, Facu lty of Engineering, Bangkok University .
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Songkran Kantawong received the M.Eng.
degree in Electrical engineering
(Telecommunication Engineering) from
Chulalongkorn University, Bangkok
Thailand. He is the lecturer in the Department of Electronics and
Telecommunication Engineering, Faculty
of Engineering Bangkok University. His is
currently working toward the PhD degree
in Electrical Engineering and also an Assistant Professor in his Department. His research interests are in
the areas of intelligent machine, image processing, pattern
recognition, fuzzy and neural network, robotics, mobile robot
relocation, wireless communication, intelligent traffic system, fire
protection, automation, manufacturing and power energy saving.
Tanasak Phanprasit is a lecturer of the Department of Electronic Engineering and
Telecommunications, Bangkok University,
Thailand. He received a B.S. degree in
Electronics Engineering from South- East
Asia University in 1990 and M.S. degree in Electrical Engineering from King
Mongkut’s University of Technology,
Thonburi in 1994. His Research interests
include image processing, neural network,
vector quantization, wavelet transform, fractal image compression and digital signal processing.