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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).
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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.
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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
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WEI ZHANG, GUO-ZHEN TAN, HUI-MIN SHI AND MING-WEN LIN 772
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
+ = − =⎧⎪= ⎨
− = − =⎪⎩
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DISTRIBUTED THRESHOLD ALGORITHM FOR VECHICLE CLASSIFICATION 773
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
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WEI ZHANG, GUO-ZHEN TAN, HUI-MIN SHI AND MING-WEN LIN 774
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 +=
= + × − − − −∑
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DISTRIBUTED THRESHOLD ALGORITHM FOR VECHICLE CLASSIFICATION 775
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−
−+==
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WEI ZHANG, GUO-ZHEN TAN, HUI-MIN SHI AND MING-WEN LIN 776
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
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DISTRIBUTED THRESHOLD ALGORITHM FOR VECHICLE CLASSIFICATION 777
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 ∈=
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WEI ZHANG, GUO-ZHEN TAN, HUI-MIN SHI AND MING-WEN LIN 778
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)(
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DISTRIBUTED THRESHOLD ALGORITHM FOR VECHICLE CLASSIFICATION 779
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
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WEI ZHANG, GUO-ZHEN TAN, HUI-MIN SHI AND MING-WEN LIN 780
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
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DISTRIBUTED THRESHOLD ALGORITHM FOR VECHICLE CLASSIFICATION 781
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.
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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-
ment of Computer Science and Engineering, Dalian University of
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-
ment of Computer Science and Engineering, Dalian University of
Technology, Dalian, P.R.C. His research interests include wireless
sensor networks and data mining, etc.