algoritmo para classificação com sensores magneticos

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JOURNAL OF I NFORMATION SCIENCE AND E NGINEERING 26, 769-783 (2010)

769

A Distributed Threshold Algorithm for

Vehicle Classification Based on Binary Proximity Sensors

and Intelligent Neuron Classifier*

WEI ZHANG, GUO-ZHEN TAN, HUI-MIN SHI AND MING-WEN LIN Department of Computer Science and Engineering

Dalian University of Technology

Dalian, Liaoning, 116023 P.R. China

E-mail: [email protected]

To improve the accuracy of real time vehicle surveillance, utilize the advances in

wireless sensor networks to develop a magnetic signature and length estimation based

vehicle classification methodology with binary proximity magnetic sensor networks and

intelligent neuron classifier. In this algorithm, we use the low cost and high sensitive

magnetic sensors to measure the magnetic field distortion when vehicle crosses the sen-sors and detect vehicle via an adaptive threshold. The vehicle length is estimated with

the geometrical characteristics of the proximity sensor networks, and finally identifies

vehicle type from an intelligent neural network classifier. Simulation and on-road ex-

periment obtains high recognition rate over 90%. It verified that this algorithm enhances

the vehicle surveillance with high accuracy and solid robustness.

Keywords: real-time traffic surveillance, vehicle detection, vehicle classification, wire-

less sensor networks, binary proximity sensor networks, intelligent neurons, distributed

threshold, adaptive, clustering

1. INTRODUCTION

Nowadays, the urban traffic became a big problem with the rapid increase of vehiclequantity, and it disturbs the normal life of urban residents and travelers. Especially the

traffic jams is a difficult problem confront the global with great financial loss every year.

Intelligent traffic control system is proved the most effective approach to resolve this

problem. Vehicle surveillance, including detection and classification, that provides real-

time traffic data for traffic light control system with the needs to optimize the spatial and

temporal allocation of traffic resource. And consequently, the performance of vehicle

surveillance is significant to traffic light control, optimal traffic resource allocation and

maintenance of the pavement system [1].

Currently there many vehicle surveillance technologies including loop sensor, video

camera, image sensor, infrared sensor, microwave radar and GPS, etc. [2, 3]. The per-

formance is acceptable but not sufficient because of their limited coverage and expensive

costs of implementation and maintenance. They have defects include line-of-sight, low

exactness, depending on environment and weather, can not perform no-stop work

whether daytime or night, high costs for install and maintenance, etc. Consequently, in

actual application the traffic data is insufficient or bad in real-timeness owing to detector

Received March 31, 2009, revised August 28, 2009, accepted September 30, 2009.

Communicated by Chih-Yung Chang, Chien-Chung Shen, Xuemin (Sherman) Shen, and Yu-Chee Tseng.* This paper was supported in part by the National Natural Science Foundation of China (No.60873256) and

National Basic Research Program of China (No.2005CB321904).



WEI ZHANG, GUO-ZHEN TAN, HUI-MIN SHI AND MING-WEN LIN 770

quantity and cost. And thus the actual performance of traffic control system such as

SCOOT/SCATS is influenced. With the increase of vehicle in urban road networks, the

vehicle detection technologies are confronted with new requirements.

Wireless sensor network is the-state-of-art technology and a revolution in remote

information sensing and collection applications [4]. Sensor node has advantages such as

low costs, small size, wireless communication, high sensing accuracy, and can be de-

ployed with great quantity. It has broad prospect of application in intelligent transporta-

tion system [5]. In the PATH (California Partners for Advanced Transit and Highways)

project of University of California, Berkeley, the possibility of replacing traditional

methods, such as loop detector, with wireless sensor networks is creatively researched.

Their ATDA ( Adaptive Threshold Detection Algorithm) is an efficient vehicle detection

algorithm with high precision of 97%, but the classification scheme is not so efficient

with low performance that overall recognition rate is below 60% [6, 7].

The length is an important distinguishing factor for vehicle classification [7, 8]. The

main challenge is that occupancy will be influenced by velocity, and consequently the

length of vehicle cannot be exactly estimated via single detector. Actually, locating and

tracking for the certain part of vehicle are indispensable when estimate the length of ve-hicle. Tracking and locating are hot topics in research of wireless sensor networks, and in

recent the approach of tracking with the geometric topology is introduced [9, 10]. Among

them the BPSN ( Binary Proximity Sensor Networks) is a simple and efficient method,

and extremely suitable for uncomplicated topology scenario such as traffic information

detection. It draws attention owing to its good performance [11-14].

Under the background, a new algorithm, Magnetic Sensors based Vehicle Classifi-

cation Algorithm (MSVCA) is developed in this paper. In this algorithm, magnetic sen-

sors are deployed as BPSN to detect the magnetic field distortion with a distributed

threshold, and estimate the length of vehicle via the geometric characteristics of the to-

pology. Finally the important features are exacted to identify vehicle type with neural

network classifier. The on-road experiment and simulation show that this algorithm en-

hances vehicle classification with good performance and solid robustness.

2. RELATED WORK

2.1 Binary Proximity Sensor Networks

The binary proximity sensor network is a special sensor network. Every sensor node

has definite coordinate and finite detection range R, and sends single bit information

about the target, detected or not, to the access point in a fixed timeslot. The master node

for computation locates and tracks target with the bits information, its own coordinate

and the geometric characteristics of the wireless sensor network [12]. It shows as Fig. 1.

Assume a BPSN with m sensor nodes, which detect target periodically in a certain

interval τ , and a m-dimensional binary vector set S as Eq. (1) is obtained. And the + 1means target moving towards the detector and − 1 means leaving away from the detec-

tion range. 0 means null state that no target is detected. The moving trace can be calcu-

lated according to this vector set and timestamp.



DISTRIBUTED THRESHOLD ALGORITHM FOR VECHICLE CLASSIFICATION 771

scope

upt downt

t/sec

r k

s k

Fig. 1. Locating and tracking with proximity sensor networks.

s(t i) ∈ (+ 1, − 1)m (1)

2.2 Vehicle Magnetic Signature and ATDA

In PATH project, they creatively use networked high precise magnetic sensors to

detect magnetic field distortion caused by moving vehicle, and introduce a traffic sur-veillance approach based on the electromagnetism that ferrous materials, such as vehicles,

distort the Earth’s field which is uniform over a wide area on the scale of kilometers [6,

7].

The Magnetic sensor such as Honeywell HMC1002 two-axis detector can measure

the magnetic field change of Earth with high accuracy. The magnetic field distortion

caused by moving vehicle is the fundamental of ATDA [7].

The arrival of the magnetic frontier and trail of the vehicle will influence the back-

ground magnetic field. As a typical distortion signal, namely the magnetic signature, raw

data r (k ) (X-axis, MICAz node) and the detection sequence s(k ) generated by ATDA are

showed as Fig. 2.

Fig. 2. Magnetic field distortion and ATDA detection result.

The raw data r (k ) is smoothed to a(k ) and input to ATDA for automatic detection

and finally generate a detection result according to the judgment of an adaptive threshold

h(k ). Here k is time interval. The output includes an impulse sequence s(k ), the corre-

sponding timestamp t up(k ) and t down(k ), and finally the detection flag d (k ) is generated

t6

X Y

Z

t1

t2

t4

t3

t5

t3t1 t2 t4 t5 t6

Z

Y

X




according to the output state of the state machine. At a finer scale, the change in detec-

tion flag occurs within 0.1s immediately after the magnetic frontier of the vehicle crosses

the sensor. Different vehicle has different ferrous structure that results differentiated

magnetic signatures with distinctive timing and amplitude characters. The magnetic sig-

nature can be used in vehicle classification.

About the classification algorithm of ATDA, there three main drawbacks as follows:

(i) the feature of vehicle signals took in classification is insufficient. Only the macro-

scopic feature (hill pattern) is considered but the timing character and influence from

velocity, viz. the duty cycle, are neglected; (ii) the length of vehicle is beyond considera-

tion; (iii) identify vehicle type directly via the amount of signal crest and hollow, thus the

capability of fault tolerance is not good when consider noise.

3. NETWORK ENVIRONMENT AND PROBLEM STATEMENT

According to the application for on-road traffic surveillance to detect and classify

vehicles traveling at the upstream of the intersections, we employ magnetic sensors anddesign a binary proximity networks to detect vehicle and estimate length based on sensor

readings of magnetic field distortion signal and an adaptive threshold.

The network topology as Fig. 3 is designed according to the traffic application sce-

nario and the characteristics of binary proximity sensor networks.

Fig. 3. The deployment of sensor nodes.

Assume to deploy n sensor nodes in the same straight line parallel with the lane that

constitutes a binary proximity network S = {S 0, S 1, …, S n-1}. The offset distance from the

lane is Doffset and the sensing range of every sensor node is R. And the distance between

node S i and proximity node S j is d ij. The AP node, which is the head of data acquisition

cluster, has more resources and capability for computing, and thus bears the synchroniza-

tion, computation, communication and topology maintenance. The sensor node reads

magnetic field distortion to detect vehicle based on ATDA and then reports the result to

AP within single hop.

(2)

Assume a vehicle runs along the parallel trace with the lane, and AP collects data in

R

AP

L

A vehicle

ijd

V

The lane

S0 S1 Sn-1

1 when ( ) 1 & ( ) 0( )

1 when ( ) 0 & ( ) 1

j j

j

j j

s t s t sq t

s t s t

τ

τ

+ = − =⎧⎪= ⎨

− = − =⎪⎩




a certain interval τ that generates the binary detection sequence sq(t j) as Eq. (2). The se-

quence s(k ) is vehicle detection status that is generated in ATDA.

The locating precision is inversely proportional to the distances among sensor nodes

[11]. To reduce node amount, the distances between nodes are belonged to normal dis-

tribution N (μ , σ ) according to vehicle length intervals. And in ATDA, the sensing range

can be adjusted via the base value of the adaptive threshold h(k ).

4. METHODOLOGY

4.1 Distributed Threshold for Vehicle Detection

To the magnetic field of the Earth, there is an uncontrollable drift because of affect

from environmental factors such as temperature, and the rate of the drift is on the order 1

measuring unit per minute. In order to account for the drift in the long term, in ATDA, an

adaptive threshold h(k ) is setup to track the background magnetic reading, which is used

to determine the adaptive threshold level for the detection state machine, as Eq. (3). Here,α is the forgetting factor, a(k ) is the smoothed magnetic signature from raw signal r (k ),

and s(k ) is the detection sequence.

(3)

In MSVCA, the adaptive threshold is shared by every node for reducing energy

consumption. The master node computes the adaptive threshold in idle state and distrib-

utes to slave nodes when vehicle is detected. All slave nodes are in sleeping state when

there no vehicle, and waked up by master node, then synchronize their timers and receive

the latest threshold as baseline and threshold to detect vehicles.

Fig. 4. State transition in state machine for vehicle detection.

The state transition in vehicle detection is showed in Fig. 4. The input parameters

include vehicle signal a(k ), threshold h(k ) and timestamp, and the output includes current

state, state sequence s(k ), detection flag d (k ) and time t up and t down.

Here M s and N s are experiential threshold to reduce effect from signal fluctuation.

( 1) (1 ) ( ) if ( ) 0( )

( 1) otherwise

h k a k sh k

h k

α α τ − × − + × =⎧= ⎨

−⎩

Init Init_done )()( k T k a p

)()( k T k a f

)()( k T k a f

s M ≥

s M p

)()( k T k a p

s N ≥

)()( k T k a f

s N ≥ Φ≥

Φp

)()( k T k a p

W T ≥ downt

Init Count0

Count01Count10

Car Count1)()( k T k a f

upt

)()( k T k a f




Time window W is used to avoid the cycle counting and vehicle missing. Meanwhile,

aiming at the abrupt fluctuation in rise and decline edge of the signal, add two temporary

states Count 01 and Count 10 and the corresponding counting threshold Φ to enhance the

robustness. In Fig. 4, there two conditions, current counting and the comparison of a(k ) and h(k ), for every state transition. In the detected state (Car ), s(k ) = 1, and d (k ) = 1 when

there continuous s(k ) = 1 detected.

4.2 Vehicle Length Estimation

For describing the current location M of target at the overlapped area, define the

event equation Γ I (t , t ) of node i, that denotes event I happened at time t on node i.

(4)

When vehicles move in detection area, the time that target enters detectable range

t enter = t i,up and the time target exits t exit = t i,down, as showing in Fig. 5. The event is definedas Eq. (4).

Fig. 5. Event in length estimation. Fig. 6. State machine for length estimation.

Simply, define the event as the leaving from the detectable range of node i. If node

p and node q have detected the events of vehicle entering and exiting the detection range

respectively, thus the length of vehicle can be estimated from Eq. (5) as follows. The

parameters R p and Rq denote the detect radius of node p and q respectively, and d i,i+1 de-

notes the distance between the sensor node i and it’s proximity node i + 1. The factor

Doffset denotes the offset distance from the pivot of sensor detect range to the lane.

(5)

Actually there difference between the magnetic length and the physic length be-

lane

Di,i+1

1+iS

M

Doffset

R i iS

s k)=1

s k)d k sq(t)

Init

Count0

Count1

Confirmed

Output s(k)=1s k =0

> Nm

d(k)=1

),(),,( qtail phead t qt p ΓΓ

t > W

Computing

Init_done

L

< Ns

niΓ

( 1), ( ),,( , )

0, otherwise

i enter i exit

I

i t t t i t Γ

+ ≤ ≤⎧⎪= ⎨

⎪⎩

2 2 2 2, 1

ˆ (1 ) ( )q

i i p offset q offset i p

L d R D R Dα +=

= + × − − − −∑




cause of the spread of magnetic line. But on other hand the exact length of vehicle is not

so meaningful, and it just needs an attribute of length that can differentiate vehicles. As-

sume the estimated length is L E , which belongs to a certain length interval separated by

the n sensor nodes, and decided by their deployment and target location.

(6)

Algorithm 1: LENGTHESTIMATION

1: Init → S; Init(interval t );

2: {h(k ), d(k), s(k), aq(t)} → buffers; 0 → T; Count0 → S; 0 → C(1);

3: while T < W do

4: if )(k a > )(k h then

5: 1 → )(k s ; Count1 → S;

6: C(1) + 1 → C(1);7: COUNT(1);

8: if C(1) < Ns then 9: Count0 → S;

10: 0 → C(1);

11: else if C(1) > Nm then 12: Confirmed → S;

13: Γ I (i, t ) → Γ ;n

i

14: if d(k) = 1 then 15: Output → S;

16: TRACK (Γ ,ni aq(t)) → L E ; L E → L;

17: Init → S;

18: end if 19: end if 20: end if

21: end while

In Eq. (5) there is a fuzzy factor α utilized to reduce the difference between the

magnetic length and physical length. Based on above, design a state machine as in Fig. 6

to detect events and then calculate the length. The processing of state machine is as Al-

gorithm 1.

The vehicles length can be estimated via Eq. (5). And consequently, the average

velocity in timeslots t occupy can be calculated according to the occupy time as well, based

on vehicle’s length and occupy time on single sensor node i [7]. The estimated velocity is

given by the following equations.

(7)

2{ | [0, ]}, E i n L L i N N C ∈ ∈ =

, , 1, [0, ], , [0, ]

q

i p q j j

j p

L D d i N p q n+=

= = ∈ ∈∑

)(

2ˆ

22

enter exit

offset i

occupy

i

t t

D R L

t D

v−

−+==




5. VEHICLE CLASSIFICATION

The intelligent neural networks is a computational model based on biological neural

networks with weighted training to non-linear differentiable function, which is widely

used in pattern recognition and classification with good performance. There are broad

applications in intelligent transportation as well [15-17].

In MSVCA, time-domain features are extracted from vehicle magnetic signature

with consideration of duty cycle and fault tolerance. After analyzing the characteristics of

vehicle magnetic signature, the enhanced feature vector set (FVS) is extracted from sig-

nals for clustering and vehicle classification. The features vector set makes up vehicle

length and time-domain features. In the meantime the two important factors, length and

velocity are taken into account, and the time feature of signals, namely duty cycle, is

sufficiently considered. In addition, intelligent neuron based classifier is used for vehicle

type recognition to quicken error reduction and enhance the fault tolerance capability.

5.1 Preprocessing and Feature Extraction

According to the principle of magnetic field distortion caused by moving vehicle [7]

and abovementioned analysis to vehicle signature detected via sensor nodes, there are

three conclusions as follows, (i) the amplitude variety is directly related with the ferrous

materials distribution of vehicle and offset to sensor node; (ii) the time-domain width of

magnetic field distortion signal is decided by length and velocity of moving vehicle; (iii)

the sensor is high sensitive so the signal is variable in amplitude and easy to be influ-

enced. To enhance the precision and overall performance of vehicle classification, all

aspects need to be taken into account.

To extract the time-domain features as Fig. 7 (a), sample data from the smoothed

signal a(k ) inside window W with frequency f (in times of reading slot), and extract the

feature sequence H f (n) with a running average to enhance fault tolerance, as showed in

Fig. 7 (b). Assume the reading frequency of sensor is ω and the running average width is2η , thus there relation between a(k ) and H f (n) as Eq. (8).

here m = Wi/ f and i ∈ [0, W / f ] (8)

Fig. 7. Features extraction from vehicle magnetic signature.

)1( f H )(i H f )(n H f

Amplitude Normalized

0

W

t/sec

2η

W

0

(b) Running average.(a) Time-domain features extraction.

1( ) ( )

2

m

f

j m

H i a jη

η η

+

= −

= ∑

t/sec




Thus with different frequency f , the H sequence can denote the variety in amplitude

and time-domain width in different extent. Above all, the Features Vector Set (FVS) of

vehicle is defined as follows.

V = ( H f (n), L) (9)

5.2 The Clustering Algorithm

Assume the class set of vehicle is C , and the FVS data set is X .

(10)

Use the improved fuzzy c-mean (FCM) clustering algorithm [18, 19], the clustering

lost function defined via membership function is denoted as Eq. (11).

(11)

The factor m j is clustering center of class c j, and b > 1 is a constant, which can ad-

just the fuzzy extent of clustering. μ j( xi) is the membership function of the ith sample to

the jth category, and it under the loose normalized restriction of Eq. (12).

(12)

Evaluate the minimum of Eq. (11) under the condition restriction of Eq. (12) with

iteration method, and obtain the membership function.

(13)

And finally the sample is classified to a certain category according to the member-

ship function of FVS. The membership value after clustering will be used as expected

output for samples of this class in neural network training, which is described in next

section.

5.3 Intelligent Neuron Classifier

The main idea of neural network is modify the weights according to the deviation

between the real output of neural network and the target vectors, and minimize the sum-mary of square deviation in output layer. According to foregoing research and experien-

tial applications [20], a three-layered neural network with infinite hidden layer nodes can

make arbitrary non-linear mapping from input to output, and thus MSVCA uses three-

layered neural networks for vehicle type recognition. The general architecture of intelli-

]},1[,{ N i x X i N ∈=

2

1 1

[ ( )] || || M N

b f j i i j

j i

J x x mμ = =

= −∑∑

1 1

( ) M N

j i j i

x nμ = =

=∑ ∑

( )

1 /( 1)

1 /( 1)

1 1

(1 / || ||)( )

1 / || ||

bi j

j i M N b

l k

k l

n x m x

x x

μ −

−

= =

−=

−∑ ∑

]},1[,{ M icC i M ∈=




gent neuron classifier used in MSVCA is as in Fig. 8 (a). To enhance the capability of

information storage on neurons and classification performance, and accelerate error re-

duction speed, the intelligent neurons are used in the classifier.

The model of intelligent neuron is showed in Fig. 8 (b). In the model, x is input,

ai are connection weights, f ( x) is sigmoid function with adjustable parameter m which

will be modified during study, and y is output.

and here (14)

The input includes FVS of magnetic signature and vehicle length, and the output is

the membership values belonging to every classes. During training, to the samples of a

certain class, the expected output is the corresponding clustering result of membership,

and thus neural network can calculate the membership function after training. A vehicle

can be classified into one predefined vehicle class in FHWA ( Federal Highway Admini-

stration, U.S.A.) scheme based on the max membership value from neural network clas-

sifier.

Fig. 8. Architecture of intelligent neuron classifier.

)(k r )(k a

)(n H f

)(k s

f

)( i f c J α

ic )(k an

)(it

L

vBand Filter ATDA

Normalized Features

Classifier Velocity

Length

Fig. 9. Block diagram of MSVCA algorithm.

5.4 The Algorithm

The overall block diagram of MSVCA is as in Fig. 9. It detects vehicle magnetic

signature signals based on ATDA and then extracts the time-domain features from mag-

netic signature of travelling vehicle. Synchronously, it estimates vehicle length via prox-

(a) Architecture of intelligent neuron classifier.

……

)0( f H

Length

…

),( ii f xc J NeuronIntelligentCommon Neuron

)1( f H

)2( f H )( x f x

…1−na

na

x

1−n x

3 x

n x

y 1a

3a

2a

…

2 x

(b) Model of intelligent neuron.

1

( )n

i

i

i

y f a x=

= ⋅∑ m xe x f

−+=

1

1)(




imity sensor networks and finally identifies vehicle and classifies to a certain class.

For sake of the limited computation power on sensor node and reduce the complex-

ity of pattern recognition in high-dimensional spaces, the PCA ( Principal Component

Analysis) can be introduced to lower the dimension of FVS [7, 21].

6. EXPERIMENTAL RESULTS AND ANALYSIS

In the on-road experiment, six MICAz nodes with Honeywell HMC1002 two-axis

linear magnetic sensors are used. The physic characteristics of sensor are listed in Table

1 and the snapshot of experiment scene is as in Fig. 10.

Table 1. Characteristics of sensor node.

Characteristics Data

Nominal Sensitivity 3.2 mV/V/Gauss

Resolution 40 μ Gauss

Supply Current < 20 mA

Operating Temperature − 40 ~ 85 Celsius

Noise Density 29 nV/Hz

Bandwidth 5 MHz

Fig. 10. Experiment scene and snapshot. Fig. 11. Reading samples (X-axis).

Five vehicle types and magnetic signatures on X-axis are obtained as databank with

sensor nodes, and the reading samples are showed in Fig. 11. And meanwhile, vehicle

types are recorded manually by eyeballing according to 13-classes FHWA vehicle classi-

fication scheme. The parameters used in experiment are listed as follows. The reading

frequency ω = 128Hz, R = 3.2m, W = 64 timeslots, f = 8, τ = 50ms, α = 0.15, Doffset =

1.8m, and the distances between sensor nodes d ij~ N (μ , σ ).

In the simulation, about 500 samples from the databank are used. The performance

of MSVCA is analyzed based on this dataset. 140 samples are utilized to clustering andtrain neural networks and use the rest to verify the performance of MSVCA. Fig. 12.

shows the clustering process in multiple iterations that both time-domain features of

magnetic signature and vehicle length are took into account. In Fig. 13, the error reduc-

tion curve (for instance of class bus) verifies the satisfactory result achieved via intelli-

gent neural network classifier.

0 5 10-20

0

20

0 5 10-20

0

20

0 5 10-20

0

20

40

0 5 10-50

0

50




Fig. 12. The processing of clustering. Fig. 13. The error reduction curve (Bus).

The statistics of recognition rate (abbr. RR) is as Table 2. It’s efficient to classify

vehicles with high precision. The experimental results show that the improvement both in

recognition rate and fault tolerance capability.

The recognition performance of several latest classification algorithms with differ-ent technologies is compared as in Table 3. It’s obvious that MSVCA improves the per-

formance of vehicle classification with wireless sensor networks and enhances the utility

of passive magnetic sensor in traffic surveillance.

Table 2. Vehicle recognition rate.

Actual type (record manually)MSVCA

C1 C2 C3 C4 C5Total

C1. (Bus) 107 2 3 3 115

C2. (Car) 122 1 123

C3. (Truck) 5 1 46 52

C4. (Van) 4 3 1 34 42

C5. (Motorcycle) 28 28

Total 116 128 50 38 28 360

RR (%) 92.24 95.31 92.00 89.47 100 93.61

Table 3. Comparison of vehicle classification algorithms.

Classification algorithm RR (%) Remarks

PATH [6][7] 60 WSN based, magnetic sensor

MSVCA 93.61 WSN based, magnetic sensor, intelligent neuron

ILD [16] 91.5 Loop sensor, BP neural network classifier

Anshul’s method [17] 86 Camera, BP neural network classifier

NN&SVM based [22] 94.8 Image sensor, BP neural network classifier

MW Sensor [23] 87 Microwave radar

Partial Gabor filter bank [24] 95.17 Camera

Repetitive pattern [25] 73.5 Satellite image

T2 FLRBC [26] > 80 Acoustic sensor, type-2 fuzzy logic classifier

0 200 400 600 800 10000

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

iteration times

e r r o r




7. CONCLUSION

Wireless sensor network is a revolution in applications of information sensing and

collection, and consequently it has broad prospect in intelligent transportation system.

This paper developed a novel vehicle classification algorithm via magnetic field distor-

tion signals, based on binary proximity sensor networks and intelligent neuron networks,

which efficiently improves the correctness and robustness and makes it possible to re-

place traditional costly technologies such as loop detector, microwave radar and camera

in traffic surveillance.

On another hand, MSVCA (including ATDA) is only suitable to normal traffic con-

dition, and in operating conditions the sensors have difficulty differentiating between

closely spaced vehicles. Under heavy traffic volume conditions, the superposition of ve-

hicle signals will influence the final performance. Vehicle detection and classification in

heavy traffic volume conditions or traffic jam is still an important and unfathomed prob-

lem that needs further research.

ACKNOWLEDGMENT

The authors would like to thank the anonymous referees for their comments and

kindly help.

REFERENCES

1. R. Alastair, et al., “Intelligent transportation system,” IEEE Pervasive Computing ,

Vol. 5, 2006, pp. 63-67.

2. A. Arora, P. Dutta, S. Bapat, et al., “A line in the sand: a wireless sensor network for

target detection, classification and tracking,” Computer Networks, Vol. 46, 2004, pp.

605-634.3. A. K. Lawrence, K. M. Milton, and R. P. G. David, “Traffic detector handbook: third

edition − volume I/II,” Federal Highway Administration, U.S. Department of Trans-

portation, 2006.

4. J. Z. Li and H. Gao, “Survey on sensor network research,” Journal of Computer Re-

search and Development , Vol. 45, 2008, pp. 1-15.

5. M. Tubaishat, P. Zhuang, Q. Qi, et al., “Wireless sensor networks in intelligent

transportation systems,” Wireless Communications and Mobile Computing , Vol. 9,

2009, pp. 287-302.

6. S. Y. Cheung, S. Coleri, et al., “Traffic measurement and vehicle classification with

a single magnetic sensor,” Transportation Research Board, 2005.

7. S. Y. Cheung and V. Pravin, “Traffic surveillance by wireless sensor networks: final

report,” Department of Electrical Engineering and Computer Science, University of California, Berkeley, 2007.

8. B. A. Coifman, “Vehicle classification from single loop detectors,” Department of

Electrical and Computer Engineering, The Ohio State University, 2007.

9. B. Marc, F. M. Jean, and S. Idrissa, “An operational model for mobile target tracking

in wireless sensor networks,” in Proceedings of the 2nd International Symposium on




Wireless Pervasive Computing , 2007, pp. 99-104.

10. K. Seema, M. Eric, and I. Volkan, “Triangulation based multi target tracking with

mobile sensor networks,” in Proceedings of IEEE International Conference on Ro-

botics and Automation, 2007, pp. 3283-3288.

11. J. Aslam, Z. Butler, F. Costantin, et al., “Tracking a moving object with a binary

sensor network,” in Proceedings of ACM International Conference on Embedded

Networked Sensor Systems, 2003, pp. 150-161.

12. W. Y. Kim, K. Mechitov, J. Y. Choi, et al., “On targets tracking with binary prox-

imity sensors,” in Proceedings of the 4th International Symposium on Information

Processing in Sensor Networks, 2005, pp. 301-308.

13. R. Beresford, N. Shriyastava, R. Mudumbai, et al., “Target tracking with binary

proximity sensors: fundamental limits, minimal descriptions, and algorithms,” in

Proceedings of the 4th International Conference on Embedded Networked Sensor

Systems, 2006, pp. 251-264.

14. J. Singh, R. Kumar, S. Suri, et al., “Tracking multiple targets using binary proximity

sensors,” in Proceedings of the 6th International Symposium on Information Proc-

essing in Sensor Networks, 2007, pp. 529-538.15. W. Wu and Q. S. Zhang, “A method of vehicle classification using models and neu-

ral networks,” in Proceedings of IEEE VTS 53rd Vehicular Technology Conference,

Vol. 4, 2001, pp. 3022-3026.

16. Y. K. Ki and D. K. Baik, “Vehicle classification algorithm for single loop detector

using neural network,” IEEE Transactions on Vehicular Technology, Vol. 55, 2006,

pp. 1701-1711.

17. G. Anshul and V. Brijesh, “A neural network based approach for the vehicle classi-

fication,” in Proceedings of IEEE Symposium on Computational Intelligence in Im-

age and Signal Processing , 2007, pp. 226-231.

18. J. G. Sun, J. Liu, and L. Y. Zhao, “Clustering algorithms research,” Journal of Soft-

ware, Vol. 19, 2008, pp. 48-61.

19.

L. G. Wu and Y. Ma, “Design of BP neural networks classifier based on modifiedfuzzy c-means clustering,” Control and Automation, Vol. 22, 2006, pp. 191-193.

20. G. Z. Tan, W. J. Yuan, and H. Ding, “Traffic flow prediction based on generalized

neural network,” in Proceedings of IEEE Intelligent Transportation Systems Con-

ference, 2004, pp. 406-409.

21. A. Gorban, B. Kegl, D. Wunsch, et al., Principal Manifolds for Data Visualization

and Dimension Reduction, Springer-Verlag, Berlin, Heidelberg, 2008.

22. S. Peter, J. Hector, T. Sameer, et al., “Automatic vehicle type classification using

strain gauge sensors,” in Proceeding of the 5th Annual IEEE International Confer-

ence on Pervasive Computing and Communications Workshops, 2007, pp. 425-428.

23. I. Urazghildiiev and R. Ragnarsson, “Vehicle classification based on the radar meas-

urement of height profiles,” IEEE Transactions on Intelligent Transportation Sys-

tems, Vol. 8, 2007, pp. 245-253.

24. P. Ji, L. W. Jin, and X. T. Li, “Vision-based vehicle type classification using partialgabor filter bank,” in Proceedings of IEEE International Conference on Automation

and Logistics, 2007, pp. 1037-1040.

25. J. Leitloff, S. Hinz, and U. Stilla, “Estimation of traffic parameters in urban areas

from satellite images,” in Proceedings of IEEE-ISPRS Workshop URBAN , 2007, pp.




1-6.

26. H. W. Wu and M. M. Jerry, “Classification of battlefield ground vehicles using

acoustic features and fuzzy logic rule-based classifiers,” IEEE Transactions on

Fuzzy Systems, Vol. 15, 2007, pp. 56-72.

Wei Zhang (張偉) received the M.S. degrees in Software

Engineering and the B.S. degree in Telecommunication Engineer-

ing from Jilin University, Changchun, P.R.C., in 2005 and 2002

respectively. He is currently working towards the Ph.D. degree in

the Department of Computer Science and Engineering at Dalian

University of Technology, Dalian, P.R.C. His research interests

include wireless sensor networks, real-time traffic surveillance,

optimal traffic control and multi-agent system, etc.

Guo-Zhen Tan (譚國真) received the M.S. and Ph.D. degree

in Computer Engineering from Harbin Institute of Technology, Har-

bin, P.R.C. and Dalian University of Technology, Dalian, P.R.C.,

in 1998 and 2002 respectively. He is a professor with the Depart-

ment of Computer Science and Engineering, Dalian University of

Technology, Dalian, P.R.C. He was a visiting scholar with the

Department of Electrical and Computer Engineering of University

of Illinois at Urbana-Champaign, IL, U.S., from Jan 2007 to Jan

2008. His research areas include network optimization, intelligent

transportation system, and wireless sensor networks, etc.

Hui-Min Shi (史慧敏) received the B.S. degree in Computer

Science and Technology from Dalian University of Technology,

Dalian, P.R.C., in 2007. She is a M.S. candidate with the Depart-


Technology, Dalian, P.R.C. Her research interests include large

scale traffic flow predication, intelligent algorithms including

artificial neural networks, Bayesian networks, support vector

machine, etc.

Ming-Wen Lin (林明文) received the B.S. degree in Computer

Science and Information Engineering from Northeastern University,Shenyang, P.R.C., in 2007. He is a M.S. candidate with the Depart-


Technology, Dalian, P.R.C. His research interests include wireless

sensor networks and data mining, etc.

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