2008 IEEE/RSJ International Conference on Intelligent Robots and Systems
Acropolis Convention Center
Nice, France, Sept, 22-26, 2008

Target Tracking Using SIR and MCMC Particle Filters by Multiple
Cameras and Laser Range Finders
Ryo Kurazume, Hiroyuki Yamada, Kouji Murakami, Yumi Iwashita, and Tsutomu Hasegawa
Abstract— This paper presents a sensor network system
consisting of distributed cameras and laser range finders for
multiple objects tracking. Sensory information from cameras
is processed by the Level Set Method in real time andintegrated with range data obtained by laser range finders
in a probabilistic manner using novel SIR/MCMC combined
particle filters. Though the conventional SIR particle filter is a
popular technique for object tracking, it has been pointed outthat the conventional particle filter has some disadvantages inpractical applications such as its low tracking performance formultiple targets due to the degeneracy problem. In this paper,
the new combined particle filters consisting of a low-resolution
MCMC particle filter and a high-resolution SIR particle filter
is proposed. Simultaneous tracking experiments for multiple
moving targets are successfully carried out and it is verified thatthe combined particle filters has higher performance than theconventional particle filters in terms of the number of particles,
the processing speed, and the tracking performance for multiple
targets.

I. I NTRODUCTION
Demand for service robots which coexist with humanand provide various services in daily life is expected toincrease more and more in next decades. However, since
the daily environment is complex and unpredictable, theserobots must have a sufficient ability to sense the changeof the environment and cope with a variety of situation.
One of the promising approaches for the robots to coexist
with human is the utilization of IT technology such as a
distributed sensor network and network robotics. The basic
idea of this approach is that robots provide a variety ofservices according to environmental information from not
only on-board sensors but also sensor networks structured
in the environment. As an empirical example of the above
approach, we have been conducting a research project named“Robot Town Project”. The aim of this research project is todevelop a distributed sensor network system covering a townsize area in which there are many houses, buildings, androads, and manage robot services by monitoring whole eventsoccurred in the town. The events sensed are notified to the“Town Management System, TMS”, and each robot receives
appropriate information of surroundings and instructions forproper services. We have already developed the prototypeof the TMS and demonstrated some applications for humanrobot cooperation according to several practical scenarios.
R. Kurazume, K. Murakami, Y. Iwashita and T. Hasegawa arewith Graduate Faculty of Infomation Sciencen and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, Japankurazume@is.kyushu-u.ac.jp
H. Yamada is with Graduate School of Infomation Sciencen and ElectricalEngineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 8190395, Japan

978-1-4244-2058-2/08/$25.00 ©2008 IEEE.

In this project, detection, tracking, and management of
position information of both human and robots in the town
are important functions of the TMS. For tracking moving
objects by cameras, we have developed the vision-based
tracking system using Level Set Method named Level Set
Tracking [1], [2]. However, several small and handy Laser
Range Finders (LRFs) have been developed and are available
at a low price in recent years, and a distributed LRF system
is becoming a practical candidate of a sensor network systemin terms of cost and performance.
On the other hand, statistical time-series approach suchas kalman filter or particle filter [3] has been attracting wide
attention as a robust tracking system of moving objects, and a
lot of literatures have been presented. Especially, the particle
filter is becoming a popular technique with the increaseof computer performance. In this method, a number ofcandidates (particles) are prepared and optimum solutions aresearched in a parallel way based on the Bayesian estimation
theory. Once a proper likelihood function of each sensor isdefined, it is easy to integrate observation information fromvarious sensors in a probabilistic manner even if a number ofheterogeneous sensors are used. However, though the particle
filter is a powerful tool for the robust tracking of a moving
target, it has been pointed out that the conventional particle
filter has some disadvantages in practical applications suchas low tracking performance for multiple targets in case thatthe number of particles is insufficient or there are too manytargets.
In this paper, we present the integrated system of visionbased and LRF-based multi-target tracking using novel
SIR/MCMC combined particle filters. Sensory informationfrom cameras is processed by the Level Set Tracking andintegrated with range data obtained by LRFs in a probabilistic manner using two kinds of particle filters with different
space resolutions, that is, a low-resolution MCMC (Markov
Chain Monte Carlo) particle filter [4] and a high-resolution
SIR (Sequential Importance Resampling) particle filter. The
proposed system makes it possible to track multiple targets
robustly against inherent sensor noise, missed detection, andmutual occlusions. Simultaneous tracking experiments for
multiple moving targets are successfully carried out andit is verified that the combined particle filters has higher
performance than the conventional particle filters in termsof the number of particles, the processing speed, and thetracking performance for multiple targets.

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II. R ELATED WORKS
LRF-based tracking systems of moving objects can be categorized into two groups: stationary LRFs in an environment[5]–[9] and on-board LRFs on mobile robot platforms [10]–[14].
For pedestrian tracking using stationary LRFs, Nakamura
et al. proposed a distributed sensor system consisting of six
LRFs (Sick LSM200) [5]. In this system, the ankle positionof pedestrians is detected by slit-like laser light and stable
tracking of individuals is performed using the kalman filter
which is designed to adjust estimated motion to naturaland periodic walking motion. Other than these methods,
many tracking systems were proposed such as occupancy
grid based tracking system [6], tracking of knee position
by kalman filter and particle filter [7], estimation of heightand position of a pedestrian using kalman filter [8], and a
reception system using a LRF and a video camera [9].
Meanwhile, several systems which utilize on-board LRFs
for the construction of an environmental map, obstacle avoidance, and target tracking have been proposed, for example,
obstacle avoidance by a wheeled chair [10], pedestrian tracking using particle filter [11], [12] and with an omnidirectional
image sensor [14].
This paper proposes the new tracking system using distributed image sensors and stationary LRFs in an environment, In this system, all the observation information fromvision and range sensors are integrated in a probabilisticmanner using two types of particle filters with different
structures, MCMC/SIR particle filters.
III. L EVEL S ET T RACKING
Level Set Tracking is a technique for tracking closed
regions in image sequences by the Level Set Method. The
Level Set Method, introduced by S. Osher and J. A. Sethian[15] , has attracted much attention as a method that realizes a
topology free active contour modeling. This method utilizes
an implicit representation of a contour to be tracked, and isable to handle the topological change of the contour intrinsically. Various applications based on the Level Set Method
have been presented including motion tracking, 3D geometricmodeling, and simulation of crystallization or semiconductor
growth. However, the calculation cost of reinitialization andupdating of the implicit function is considerably expensive
as compared with the cost of conventional active contour
models such as “Snakes”. On the other hand, we haveproposed an efficient calculation algorithm for the Level
Set Method named the Fast Level Set Method (FLSM), anddemonstrated real time tracking on video images [1] or three
dimensional motion capture system [2].
By applying FLSM to target tracking problem in 2D image
sequences, multiple closed regions, extracted by backgroundsubtraction for example, can be tracked quite robustly against
spike noise in sensor data. Even if separate regions areoverlapped temporarily, these regions are merged to a single
region and separated again quite naturally. An example ofLevel Set Tracking for two pedestrians is shown in Fig.1. The
image size and the processing speed are 320 × 240 pixels

and 60 Hz, respectively. In this example, two closed contours
are merged to a single closed contour by the overlap of two
people, and after a short period, the single closed contour isseparated into two closed contours again. This indicates that
the Level Set Tracking can handle the topological change
caused by the overlap of multiple regions.

t=0s

t=0.77st=1.60st=1.82sFig. 1.

Level Set Tracking

IV. T RACKING SYSTEM USING DISTRIBUTED VIDEOCAMERAS AND LRF S
Though the Level Set Tracking enables to track moving
objects in video images robustly for sensor noise and mutual
occlusion, the precise estimation of 3D position and thecorrect separation of occluded objects are difficult problems
for a monocular camera. On the other hand, slit scanning type
LRFs (Sick LMS200, Hokuyo URG04LX) can acquire thedistance information from the sensor to the target directly.
The slit scanning type LRF exposes slit-like projection lightby a one-axis mirror, and measures the distance from thesensor to the object in a 2D plane. Therefore, by integrating
range data and tracking information by the Level Set Tracking in video images, the robust motion tracking system which
estimates the relative position of occluded objects correctly
can be developed. In this section, we introduce the proposed
tracking system using the vision and range sensors.
A. System configuration
Figure 2 shows the configulation of the tracking system. A
sensor unit consisting of a video camera and a LRF is connected to a local computer and distributed in an environment
as a sensor node. The video camera and the LRF are installed
Environment
Sensor nodeHub

Sensor nodeSensor node

Host computer

Sensor nodeSensor nodeSensor nodeSensor nodeSensor unit
ComputerCameraLaser range finder

Fig. 2.

Tracking system using distributed sensor units

to a base unit so that the optical axis of the camera is parallelto the slit-like laser light of the LRF (Fig.3). The position3839

of the sensor unit in the environment is calibrated precisely
beforehand. The local computers are connected to a hostcomputer via Internet and sensory information is integratedusing two types of particle filters (a low-resolution MCMC
particle filter and a high-resolution SIR particle filter) in thehost computer. Figure 4 shows the basic concept of data
fusion of camera images and range data. Solid and dashedlines indicate the range data from the LRF and silhouette
regions in the camera image, respectively. By fusing two
kinds of sensor data, sensor noise in both sensors, especiallyunstable range data around occluding edges, is suppressedas shown in Fig.5.
Laser range finders
Hokuyo URG-04LX
SICK, LMS200

IEEE 1394 Cameras
PGR, Dragonfly2

Fig. 3.

Sensor unit
Camera image
Targets
Noise

Range data of targets
detected by LRF

Z̃ti = Zti , ( zsj − ztj > Tr ; j = 1 ∼ M )Stationary range data
(Environment)

Silhouette areas
in camera image

B. Processing flow of the proposed system
In this section, we explain the processing flows of the
proposed system in the sensor unit and the host computer,separately. We assume that the number of moving objects
is unknown and each particle of both particle filters has thestatus about position and velocity of a single target.
1) Processing flow in the sensor unit: At first, the sensor
unit i measures the environment in which there is no moving
target, and obtains the information concerning the static environment. The obtained information of the static environmentis a background image from the camera Isi (u, v) and static
range data Zsi = {zs1 , . . . , zsM } from the LRF to stationary
objects such as walls, doors, or furnitures.
Moving objects at time t are detected through the following two steps.
1) Apply the Level Set Tracking for detected regions obtained by the background subtraction for the captured
image Iti (u, v) using the background image Isi (u, v).
Find the left and right coordinates ul and ur of
the jth detected contour and calculate the azimuth
angles θtj = {θl , θr } of the contour j in which thejth target is involved as shown in Fig.6. If multiple
regions are detected, the azimuth angles are obtainedas Θit = {θt1 , θt2 , . . . θtm }, where m is the number ofthe detected regions.
2) Extract discrete points Z̃ti from range data Zti at time
t which are separated Tr away from the static range
data Zsi .
(1)

The above tasks are repeated independently in each sensor
unit, and provide the most recent information to the hostcomputer according to the demand command.

Noise
Noise

Moving object

Fig. 4.

Information fusion of range data and camera images
Image plane

Moving object

Room

LRF andcameraLRF

Contour

Silhouette area
Focal pointfθlul u rθr
Fig. 6.

Noise
Fig. 5.
data(a) LRFs(b) LRFs and cameras

Suppression effect of noise by fusing camera images and range

Existing region of a target

2) Processing flow in the host computer: The host computer integrates the information from the sensor units andestimates the positions of the moving objects. The details
are as follows:
1) Send demand commands to the sensor units sequentially and obtain the sensor data Θit and Z̃ti . Then,integrate the sensor data from sensor units as Θt ={Θit }, Z̃t = {Z̃ti }. Θt consists of the sets of the areaof objects in all images. On the other hand, Z̃t consistsof all the range data detected by the sensor units. We
do not consider which sensor the range data is obtained
from.

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2) The target positions are estimated using the combined
particle filters. Details of the combined particle filters
are shown in the next section.
C. Data integration using MCMC/SIR combined particle
filters
In the host computer, the tracking information by the Level
Set Tracking and range data are integrated in a probabilisticmanner using two types of particle filters, the low-resolution
MCMC particle filter and the high-resolution SIR particle
filter.
The SIR particle filter is a popular technique which has
been used in many tracking applications. As is well known,
the SIR particle filter is robust for missed detection andeasy to integrate tracking information from multiple sensors.
However, there is a well-known problem in case of multiple
target tracking. If there are multiple targets in the scene but
plenty of particles are not used, particles concentrate to a
few targets and tracking of other targets fails. This problem isknown as “Degeneracy problem” [3] (Fig.7), and an inherent
problem of the SIR particle filter which generates new particles around the particles having large weights intensively.
To avoid this problem, some extensions of the particle filter

Taking these features into acount, we propose the combined system of these two particle filters which are definedin different space resolutions. The basic idea is as follows:
new particles of the SIR particle filter are generated around
not only the particles having large weight but also theparticles of the MCMC particle filter. In addition, we set
that the resolution of the tracking space of the MCMC
particle filter is lower than the one of the SIR particle filter
since the MCMC particle filter cannot handle objects moving
at high speed in high resolution space. The convergent
process of the MCMC particle filter is repeated from initial
particle positions distributed uniformly at every update timeof the SIR particle filter. The idea of the combined particle
filter explained above is illustrated in Fig.8. The proposed
High resolution SIR particle filter

Low resolution MCMC particle filter

Fig. 8.

Camera image
Targets
Noise
SparseDense

Fig. 7.

Degeneracy problem

algorithm have been proposed, for example, Resampling
move method [16] which utilizes the state transition based on
MCMC and SIR sampling recursively, Regularized particle
filter [17], the use of Gibbs sampler [18], and the Mixture
particle filter [19]. However, the convergence speed of thesetechniques is low or the number of particle filters must bechanged according to the number of targets. Therefore, it isdifficult to apply these techniques for real time applicationsor the algorithm becomes complex if objects leave and enter
the scene.
Meanwhile, the MCMC particle filter [4] is based on the
Metropolis-Hastings which is a weak resampling technique,and thus, the convergence speed to concentrate particles
around the target posterior is lower than the SIR particle filter
which is based on the importance resampling. No particles
are killed and reproduced. However, the MCMC particle
filter shows good performance for tracking of multiple targets
since it converges to all the targets uniformly thanks to therandom sampling.

The combined particle filter

combined particle filter has both features of high tracking
accuracy by the SIR particle filter and good performancefor the simultaneous tracking of multiple targets without
undetected targets by the MCMC particle filter. Therefore,
the proposed filter can track multiple targets with a small
number of particles robustly even if the number of targets is
unknown and changed during the tracking. In addition, since
the number of particles can be reduced, the processing speed
is also improved as shown in the following experiments.
The details of the combined particle filters are discribedfollow. The event Xt indicates that the moving object islocated in position xt at time t. In both particle filters, eachparticle has a hypothesis and its likelihood. The posterior
probability p(Xt |Θt , Z̃t ) after obtaining the observation Θt
and Z̃t can be estimated recursively using particles. Here, Θt
and Z̃t indicate the events that sensory data Θt and Z̃t are
obtained from the video camera and the LRF, respectively.
The difference of the MCMC and the SIR particle filters is
the way to choose particles at next time t + 1 according to
the current likelihood.
1) SIR particle filter: The procedure of the SIR particle
filter is as follows:(1) Generation of initial particles
(n)
(n)
(n)
(n)
Generate N particles s0 = {x0 , v0 , w0 }
(n)
(n)
(n)
(n = 1 ∼ N ). Here, xt , vt , wt are theposition vector, the velocity vector, and the weightat time t, respectively.
(2) State transition
Apply the motion model p(Xt |Xt−1 ) to the particles and move them to the next status. In this paper,
we utilize the linear uniform motion as the motion

3841

model described as
(n)
(n)
(n)
xt = xt−1 + vt−1 Ts(2)where Ts is a sampling interval and w .
(3) Likelihood calculation
Calculate likelihood p(Θ, Z̃|Xt ) for each particle
according to the following equation.
p(Θt , Z̃t |Xt ) = p(Θ|Xt )p(Z̃|Xt )
where

(n)

p(Θt |Xt ) = f (xt )2√p(Z̃t |Xt ) = exp(−d /2σs2 )/

(3)
(4)

2πσs

(5)

and σs is a position error, d is minimum Euclidean(n)distance between xt and Z̃t .
(n)d = min  xti

− zti (6)(n)

f (xt ) is a function which returns the constantvalue S (0 ≤ S ≤ 1) if the azimuth angle(n)to xt is in the area of Θt , and 1 − S if it(n)
is not. Instead of this simple function f (xt ),we can utilize some statistical functions such asa Gaussian kernel. In this paper, we choose thesimplest function explained above in order to obtainthe constant value if occlusion occurs. This point
will be discussed later.
From Eqs.(3) to (6), we determine the weight ofeach particle as(n)
wt = p(Θt , Z̃t |Xt )

(7)

(all)
Then, the sum of the weight of all particles wt
=
N
(n)
w
is
calculated.
n=0 t(5) Resampling(n)Choose N P (0 < P < 1) particles stfrom the N particles according to the probability
(n)
(all)
(n)
wt /wt , and generate new particles st+1 byadding random noise. N (1−P ) particles are newly
generated around the estimated target positions by
the following MCMC particle filter.
(6) Position estimation
Estimate the positions of the moving objects basedon the distribution of particles. At first, all theparticles are divided into several groups by k-meansclustering algorithm. If the number of particles in
one group is larger than the threshold, the positionof the moving object is estimated as the weighted
mean of the positions of particles in the group.
2) MCMC particle filter: The MCMC particle filter isquite similar to the SIR particle filter. The only thing thatis different is the resampling procedure in step (5).
(5) Resampling(n)Choose N P (0 < P < 1) particles st fromthe N particles randomly and generate candidates
(n)
(n)
of new particles st+1 . Then, the weight wtofeach candidate is calculated according to the Eq.(7).

Next, by applying the Metropolis-Hastings using
(n)
(n)
(n)
the weight ratio wt /wt , new particles st+1(n)
are generated. wt is the weight before applying
the state transition in step (2). On the other hand,N (1 − P ) particles are generated uniformly in thewhole space so that new targets can be detected
and tracked.
As for the occlusion problem, since range data fromdifferent sensors are all processed without distinction of
sensor units in 2D plane, the tracking performance is notaffected seriously if the range data of the object is obtained
sufficiently from the other sensors which can see the object.
In addition, even if the object is occluded by front objects
in camera images, the likelihood calculated in Eq.(4) is
unchanged since the occluded object is also in the regionof the area of the occluding objects in images. The performance of the proposed system for the occlusion problem isverified through the tracking experiments with a number ofpedestrians in section V.
V. E XPERIMENTS
This section introduces the experimental results using
the propose distributed sensor system and the combined
particle filters. The performance of the proposed combined
particle filters is evaluated by computer simulations andthe simultaneous tracking experiments of multiple targets in
actual environments, respectively.
A. Simulation experiments
Firstly, we tested the performance of three types of particle
filters in the case that three targets begin to move, overlap
each others, and separate again on a 2D plane. The particle
filters tested are a) the conventional SIR particle filter, b)
the Mixture particle filter [19] for the tracking of multiple
targets, and c) the proposed MCMC/SIR particle filter.
Figure 9 shows the experimental results. Three objects
begin to move from the different initial positions toward
the same target position simultaneously. The numbers ofparticles are, a) 900 for the SIR particle filter, b) 900 (300× 3) for the Mixture particle filter, and c) 600 (300 for
MCMC and 300 for SIR) for the proposed MCMC/SIR
particle filter. In the experiment, the number and initial
positions of the objects are assumed to be unknown except
the Mixture particle filter. Only for the Mixture particle filter,
we assign the initial 300 particles uniformly for each object,
respectively, before stating the simulation.
As shown in Fig.9, a) all the particles of the conventional
SIR particle filter converged to the single object just after
stating the simulation. As for b) the Mixture particle filter,each object was tracked separately by each particle filter until
the objects crossed each other. However, after the overlap
and separation, all the particles were converged to the single
object and other objects could not be tracked appropriately.
On the other hand, the proposed MCMC/SIR particle filter
with the least number of particles was able to keep tracking
all the target stably even after they overlapped and separated.

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ImageTargets

Tracking results

Sensors

Tracking result top view

Sensors

11(a) SIR particle filter (Number of particles is 900)

2

3

5
4

1
3

2

2

3
1

5

3

3
5

2

(b) Mixture particle filter (Number of particles is 300 3)

1

1

4

2

3

2

1

4

3
2

(c) SIR/MCMC particle filter (Number of particles is 300

1

2)

1

3
4

Fig. 9.

4

Simulation experiments for multiple targets

Next, we conducted the tracking experiment in an actual
house shown in Fig.10. Four sensor units (Dragonfly2x4,
LMS200x4) are placed in four rooms on the first floor of
the house (Fig.11). In the experiment, up to five people
waking around the first floor are tracked simultaneously.
The numbers of particles of the SIR and MCMC particle
filters are 2000 and 1000, respectively. Figure 12 shows theexperimental results. Left, middle, and right columns indicate
the camera images, tracking results from the same viewpoint,
and tracking results from the top view. From this experiment,
it is verified that all the people including the one sitting on
the chair can be tracked appropriately using the proposed
distributed sensor system.

5

4

3

1

2

3

1

B. Tracking experiment in real environment

5

3

3

1

4
4
3
1
4

3

Fig. 12.

3

2
5

1

Tracking results in the houseand Fig.14 shows the images of the experiments (left column)
and tracking results (right column). By comparing the true
positions measured by the total station (TOPCON, GTS825A) and hand-held corner cubes, the average error and thestandard deviation of these paths were 86.9 [mm] and 59.7[mm], respectively. The processing time for the tracking of
11 people was 32.6 [ms] in the case that the MCMC and SIR
particle filters are updated 10 times and 1 time, respectively.
[mm]
3000

0

OutwardFig. 10.

IndoorExperimental house for the robot town project
Tracking trajectories

10000mm-5000

Sensor nodes-5000

Fig. 13.

7000mmTable

Stairs:Sensor node (LMS200+DragonFly2)

Fig. 11.

Sensor positions in the house

C. Tracking experiment for a number of moving objects
Next, we placed four sensor units (Dragonfly2x4,
LMS200x4) in a same room of 14[m] × 10[m], and tracked
up to 11 people simultaneously. The numbers of particlesof the SIR and MCMC particle filters are 2000 and 1000,
respectively. Figure 13 shows the walking paths of 11 people,

0

5000

[mm]

Paths of 11 targets

Figure 15 shows the comparison experiments of the SIR
particle filter, the Mixture particle filter, and the proposed
SIR/MCMC particle filter. The numbers of particles of the
SIR, the Mixture, and the proposed SIR/MCMC particle
filters are 6000, 6000 (=2000x3), and 3000(2000 for SIR and1000 for MCMC), respectively. Three objects appeared fromdifferent positions sequentially, got together, and separated
and disappeared. Figure 15 shows that only the proposed
combined particle filters could track the objects even after
they dispersed. The processing time for the SIR and the
Mixture particle filters were 19.8 [ms] and 19.6 [ms], respectively. On the other hand, the processing time of the
proposed MCMC/SIR particle filter was 17.1 [ms].

3843

ACKNOWLEDGMENT
87.7s

This research was supported in part by the Ministry of
Education, Science, Sports and Culture, through a Grant-inAid for Scientific Research (B) (No.19360119).

89.3s

R EFERENCES

91.0s

92.7s

94.3s

96.0s

Fig. 14.

Tracking results of 11 targets by SIR/MCMC particle filters

ImagesSIR

MixtureSIR/MCMC

miss

miss

Fig. 15.
filters

Experimental results of SIR, Mixture and SIR/MCMC particle

From all the experiments explained above, it was verified
that a set of combined particle filters is able to keep tracking
the unknown number of people who appear and disappear at
unknown positions, appropriately.
VI. C ONCLUSION
This paper proposed a new tracking system of multiple
moving objects using Level Set Tracking and multiple laser
range finders. Sensory information from distributed sensor
system is integrated by the combined particle filters consisting of the low-resolution MCMC particle filter and thehigh-resolution SIR particle filter, sequentially. Simultaneous
tracking experiments for multiple moving targets are successfully carried out and it is verified that the combined particle
filters has higher performance than the conventional particle
filter in terms of the number of particles, the processing
speed, and the tracking performance for multiple targets.

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