vehicle accident detection and identification using image

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];

[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

mailto:[email protected]

mailto:[email protected]



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



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.



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



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



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



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




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


กข 2213 4 DD D1 33 33 35 35 C0


ทม 6798 5 AE A1 22 22 28 29 A0


จช 791 5 BB C5 41 41 49 49 B1


ดต 251 4 ED E3 30 30 37 37 C0


ชพ 791 5 AE C0 35 35 38 B5 C8


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.


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



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 .

REFERENCES

[1] Al-Khateeb, K., Johari, J.A.Y., “Intelligent dynamic traffic

light sequence using RFID,” ICCCE 2008 International

Conference on Computer and Communication Engineering,

2008., Volume, Issue, 13-15 May 2008.

[2] A. Chattaraj, S. Chakrabarti, S. Bansal, S.Halder and

A.Chandra, “Intelligent Traffic Control System using RFID, ” National Conference on Device, Intelligent System and

Communication & Networking (AEC-DISC 2008), 10-13,

2008.

[3] Tran Systems corp., “Statewide Transit Intelligent

Transportation Systems Deployment Plan,” Iowa Department

of Transportation, May 2002.

[4] Finkenzeller K, RFID Handbook: Fundamentals and

Applications in Contactless Smart cards and Identification

(2002).

[5] Klaus Finkenzeller. RFID Handbook: Radio-Frequency

Identification Fundamentals and Applications. Wiley, New

York, 2000.

[6] M. Antonini , M. Barlaud, P. Mathieu and I. Daubechies,

“Image Coding Using Wavelet Transform,” IEEE Trans.

Image Processing, vol.1, no.2, pp.205-220, 1992.

[7] A. Abadpour, S. Kasaei, “A new principle component

analysis based Colorizing method,” in: Proceedings of the

12th Iranian Conference on Electrical Engineering

(ICEE2004), Mashhad, Iran, 2004.

[8] RFID Handbook: Fundamentals and Applications in Contact

less Smart Cards Identification, 2ed, Klaus Finkenzeller,

Wiley, 2003.

[9] Penttila K, Sydanheimo L, and Kivikoski M (2004)

Performance development of a high-speed automatic object

identification using passive RFID technology, Proceedings of

the 2004 IEEE International Conference on Robotics &

Automation, 4864-4868.

[10] Rovid, A & Melegh, G., “Modeling of road vehicle body deformation using EES values detection,”. Proceedings of the

IEEE Conference on Intelligent Signal Processing, pp. 149-

154, 2003.

[11] M. Kim and N. Y. Chong, “Rfid-based mobile robot guidance

to a stationary target,” in Mechatronics, 2007.

[12] Kubota, S., Okamoto, Y., and Oda, H., “Safety Driving

Support System Using RFID for Prevention of Pedestrian-



involved Accidents,” 6th International conference on ITS

Telecommunication Proceedings, 226–229, 2006.

[13] Rabie, T., A. Shalaby, B. Adbulhai and A.E. Rabbany,

“Mobile vision-based vehicle tracking and traffic control,”

Proceeding of the 5th International Conference on Intelligent

Transportation Systems, Sep. 3-6, IEEE Xplore Publishing,

USA., pp. 13-18, 2002, DOI: 10.1109/ITSC.2002.1041181.

[14] Albagul, A., M. Hrairi, Wahyudi and M.F. Hidayathullah,

“Design and development of sensor based traffic light system,” Am. J. Applied Sci., 3:1745-1749., 2002,

http://www.scipub.org/fulltext/ajas331745-1749.pdf.

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.

http://www.scipub.org/fulltext/ajas331745-1749.pdf

vehicle accident detection and identification using image

Documents