Distance-based Camera Network Topology Inference for Person Re-identification
Yeong-Jun Cho and Kuk-Jin Yoon
Computer Vision Laboratory, GIST, South Korea

arXiv:1712.00158v1 [] 1 Dec 2017

{yjcho, kjyoon}@gist.ac.kr

Abstract

ID:337
1.23m/s

In this paper, we propose a novel distance-based camera network topology inference method for efficient person
re-identification. To this end, we first calibrate each camera and estimate relative scales between cameras. Using
the calibration results of multiple cameras, we calculate
the speed of each person and infer the distance between
cameras to generate distance-based camera network topology. The proposed distance-based topology can be applied
adaptively to each person according to its speed and handle
diverse transition time of people between non-overlapping
cameras. To validate the proposed method, we tested the
proposed method using an open person re-identification
dataset and compared to state-of-the-art methods. The experimental results show that the proposed method is effective for person re-identification in the large-scale camera
network with various people transition time.

ID:612
4.01m/s

ID:578
2.04m/s

60 Distribution of

person’s speed

ID:337

ID:494

40

0.51m/s

20
0
0

ID:494
1

ID:612

ID:578
2

3

4
(m/s)

Figure 1. Challenges in person re-identification based on
the time-based camera network topology due to the diverse
speeds of people. Each blob was marked at every 0.3 seconds interval and each color indicates a person identity.
nectivities between cameras) into account; therefore, they
have to examine every possible person candidate in the camera network to re-identify a person. Instead of examining
every person, we can restrict and reduce the search space
by inferring the spatio-temporal relation between cameras,
referred to as a camera network topology.

1. Introduction
Numerous surveillance cameras installed in public
places (e.g., offices, stations, and streets) allow monitoring and tracking of people in large-scale environments.
However, it is difficult for a person to individually observe each camera. To reduce the human efforts, person reidentification techniques which automatically identify people between multiple non-overlapping cameras can be used.
Previous works have mainly focused on modeling people
appearance information such as feature descriptor extraction [11, 21] and learning similarity metrics [10, 16] to perform person re-identification. Recently, many appearance
modeling methods based on deep neural network [1, 42]
have been proposed.
However, in a large-scale camera network where the
number of cameras and the number of people are large, the
person re-identification problem becomes very challenging. In particular, it is difficult to effectively perform reidentification only by using the appearance-based methods
in the large-scale camera network. This is because the conventional appearance-based methods do not take the structure of the camera network (e.g., spatial and temporal con-

In recent years, several camera network topology inference methods [5, 26, 32] have been proposed. In general,
camera network topology represents spatio-temporal relations and connections between cameras. The topology is
represented as a graph Gt = (V, Et ), where vertices V
denote cameras, and edges Et denote the transition distribution of people across cameras according to ‘time’. We
name this topology Gt as a time-based camera network
topology. Although there has been much progress in person re-identification using camera network topology, the
time-based topology has difficulty in dealing with the diverse walking speed of the people. As shown in Fig. 1,
the walking speed of people is very diverse. For example,
if a person walks much faster or slower than the average
speed, it is hard to predict its transition time between two
non-overlapping cameras based on the time-based topology.
To overcome the limitations of the previous works, we propose a novel distance-based camera network topology in1

ference method to perform efficient and accurate person reidentification.
To estimate the speed of a moving person, we need to
calibrate cameras in a network automatically. The progress
in self-calibration methods [22, 25] enables us to estimate camera parameters without any off-line calibration
steps [43]. However, the estimated camera extrinsic parameters (i.e., camera position t) are not accurate due to scale
ambiguity in the self-calibration method, i.e. we can obtain the camera extrinsic parameters determined up to scale.
In this work, we first estimate the relative scale between
cameras based on human height information and correct
the inaccurate camera calibration results. We then calculate speeds of all people in the camera network. Subsequently, we infer the distance between cameras by multiplying the speeds and transition times of people and build
a distance-based camera network topology. The inferred
distance-based topology can be applied adaptively to each
person – when we divide the distance-based camera network topology according to the speed of a person, it gives a
person-specific time-based camera network topology.
The main idea of this work is simple but effective. To the
best of our knowledge, this is the first attempt to infer the
distance-based camera network for person re-identification.
To validate the proposed method, we tested the proposed
method using the SLP [6] dataset and compared with stateof-the-art methods. The results show that the proposed
method is promising for person re-identification in the
large-scale camera network with diverse speeds of people.
The rest of the paper is organized as follows: In Sec. 2,
we review previous works of person re-identification and
camera network topology inference. In Sec. 3, we describe
our proposed distance-based camera network topology inference and person re-identification methods. The dataset
and evaluation methodologies are described in Sec 4. The
experimental results are reported in Sec. 5 and we conclude
the paper in Sec. 6.

identification task. In addition, several works exploited additional information such as human pose prior [7, 33, 39]
and group appearance model [20, 47] to improve the reidentification performance.
In recent years, many re-identification methods based
on learning deep convolutional neural network (CNN) [34,
40, 45] and Siamese convolutional network [1, 8, 36, 42]
have been proposed for simultaneously learning both features and metrics. In addition, several works utilized recurrent neural network (RNN) [28, 48, 49] and long short term
memory (LSTM) [41] network to perform multi-shot person
re-identification. Although a lot of person re-identification
methods have been proposed so far, the challenges of person re-identification in a large-scale camera network, e.g.,
spatio-temporal uncertainty between non-overlapping cameras and high computational complexity, still remain.

2.2. Camera Network Topology Inference
Recently, many works have tried to infer a camera network topology and employ camera geometry to resolve the
spatio-temporal ambiguities. Several works [4, 14, 30] assumed the camera network topology is given and showed
the effectiveness of the spaito-temporal information between cameras. However, in practice, the camera network
topology is not given. Thus, many works have tried to infer
the camera network topology in an unsupervised manner.
Makris et al. [26] proposed a topology inference method
based on a simple event correlation model between cameras. This topology inference method was extended in many
works [5, 29, 32]. Similarly, Loy et al. [23, 24] inferred a
camera network topology by measuring mutual information
between activity patterns of cameras.
The previous topology inference methods [5, 23, 24,
26, 32] are practical since they do not require appearance
matching steps such as re-identification or inter-camera
tracking for topology inference. However, the inferred
topologies are prone to be inaccurate since the topology is
easily contaminated by false event correlations, which frequently occur when people pass through blind regions irregularly. On the other hand, several works [3, 6, 27] inferred
the camera network topology using person re-identification
results. These methods can be more robust to noise than the
event-based approaches since the methods infer the topology by utilizing true correspondences between cameras. Especially, Cho et al. [6] iteratively solved re-identification
and camera network topology inference. It achieved accurate results in both tasks thanks to its iterating strategy.
The previous camera network topology inference methods, which have been proposed so far, mainly focused on to
infer the transition time of people between cameras. However, the previous methods, so-called, ‘time-based topology’ inference methods cannot efficiently handle the diverse
speeds of people as shown in Fig. 1.

2. Previous Works
2.1. Person Re-identification
In general, most of the person re-identification methods
rely on appearances of people to identify people across nonoverlapping views. To describe and classify the appearances of people, many works have tried to propose appearance modeling methods such as feature learning and metric learning methods. For the feature learning, [11, 21, 44]
designed feature descriptors to well describe the appearance of people. For the metric learning, many methods [10, 16] have been proposed and used for person reidentification [9, 31, 38]. Several works [16, 31] extensively evaluated the feature and metric learning methods to
show the effectiveness of those methods in the person re2

800
600
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200
0

800
600
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0

Distance-based camera
network topology inference

𝑠

𝑘𝑙

0
-1000
Y

parameters

K [R t]

2000
0

1000
X

distance

-1000
Y

1000
0

People
speeds

0.5
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3.2 m/s Distance =
0

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800

1000

1000

3.0 m/s

500
0

-500

-1000

(Sec. 3.2)
X

speed × time

Person re-identification via
distance-based topology
𝑝𝑖 (Δ𝑡)

People speeds
in CAM l
𝐺𝑑 = (𝑉,𝐸𝑑) 𝑝(𝑑)
1.6 m/s

1500

1

0
-1000

1

Camera

1000
2000

1000

People speeds
in CAM k 𝑝(𝑑)
1.4 m/s

3 4
ID: 1 2

2000

2000

Re-id
results

People heights
in CAM l

Distance
-based
topology

-500

-400

-200

0

200

400

600

800

1000

𝑣𝑖 , 𝑣𝑗

(Sec. 3.3)

𝑣𝑖

distance

Time

𝑣𝑗 𝑝 Δ𝑡
𝑗

Get adaptive
search range

Perform re-id

People heights
in CAM k
4
ID: 1 2 3
Z

Z

Re-identification results

Relative scale estimation
between cameras

Time

(Sec. 3.4)

Figure 2. Overview of the proposed framework: distance-based camera network topology inference and person reidentification.

3. Proposed Methods

topology as described in Sec 3.3. Finally, we perform person re-identification based on the inferred distance-based
camera network topology in Sec. 3.4. The proposed framework is illustrated in Fig. 2. For reproducibility, the code of
this work is available to the public at: https://.

As mentioned above, many camera network topology inference methods have been proposed recently to perform efficient person re-identification in a large-scale camera network. They inferred a time-based camera network topology
based on the people transition time between cameras. However, the speed of people can be very diverse, and it leads
diverse people transition time between cameras as shown in
Fig. 1. Therefore, the conventional time-based topology becomes ambiguous and inaccurate under the diverse speed of
people.
Actually, in surveillance videos, it is possible to extract
additional cues such as camera parameters (position and
viewpoint) and trajectories of people. Using the additional
cues, we can also estimate walking speed of each person.
Then, the challenge of re-identification due to diverse walking speed becomes more tractable. In this work, we fully
exploit those additional cues and propose a new distancebased camera network topology inference, which does not
depend on the speed of a person.

3.2. Relative Scale Estimation between Cameras
In a general pinhole camera model, the relation between
a 2D image (pixel coordinates [u, v]) and a 3D point (world
coordinates [X, Y, Z]) can be represented by 3× 4 projection matrix P as
>

>

[u, v, 1] = P [X, Y, Z, 1] ,
P = K [R

t] ,

(1)

where K, [R t] represent camera intrinsic and extrinsic
parameters. Unfortunately, most surveillance cameras remain uncalibrated.
In order to estimate the camera parameters, camera selfcalibration techniques [22, 25] can be employed. These
methods do not require any pre-defined checkerboards and
off-line calibration tasks [43]. Instead of using a checkerboard, they utilize a human height H as the checkerboard.
A camera extrinsic parameter t (camera position) is determine by the value of H. However, H is unknown in general; thus Liu et al. [22] set H to any pre-defined specific
value (e.g., the average height of humans: H=1.72m [35]).
Therefore, although we can estimate the intrinsic and extrinsic parameters of each camera using the self-calibration
technique, the camera extrinsic parameter t is not accurate
since we use the inaccurate H value. In this work, we consider every camera in the camera network; thus each camera
extrinsic parameter t should be adjusted to share the same
world coordinate system.
To this end, we estimate relative scale of human heights
between cameras based on person re-identification results
and adjust each camera’s extrinsic parameter t. We denote
a person i in camera k as oki . Then, a matrix of people

3.1. Overall Proposed Framework
We first obtain initial person re-identification results (i.e.,
person correspondence pairs) between cameras. To this end,
we can apply any existing re-identification methods except
for methods using prior knowledge of the camera network1 .
The initial re-identification results are utilized in following proposed steps. In Sec. 3.2, we perform the relative
scale estimation for each camera. Each camera in the camera network is calibrated using camera self-calibration techniques [22,25] and its camera parameters are adjusted based
on the proposed method. After estimating relative scales
between cameras, we calculate walking speeds of people in
each camera. The speeds of people and re-identification results are used to infer the distance-based camera network
1 For example, person re-identification based on metric learning [10,16]
requires true person correspondences between cameras to learn the distance metric. We aim to run our framework without any prior knowledge
of the camera network. Thus we do not use the methods requiring prior
knowledge of the camera network.

3

CAM A

Unit (m)
4

ID:13
Z

ID:7

ID:8

ID:1
ID:5
Head point
Foot point

800
600
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Z

ID:13

4

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1000

X

2D image

ID:7

ID:1

00

-2
-1000

Y

ID:5

-2-1000

00

Y

21000

42000

3D world

correspondences is defined as

Mkl = (mij ) |1 ≤ i ≤ N k , 1 ≤ j ≤ N l ,
(
1 if oki corresponds to olj
,
mij =
0 otherwise

S kl = arg min
S

l

i=1 j=1

mij

H(oki )
S−
H(olj )

1.9 m/s

ID: 2 0.8 m/s

1.0 m/s
∆𝑡=46

1.2 m/s

Figure 4. Example of estimating a distance between two
cameras. The speed in the blind area is inferred by averaging two speeds from two cameras. The distance between
cameras is estimated as 46m from both identities.
calculated as follows,
T q
2
2
1X
k −Yk
k − Xk
+ Yi,t
Xi,t
v̄ik =
i,t−1 , (5)
i,t−1
T t=1

(2)

where the t is the time (second), and Xi ,Yi are the world
coordinates (meter) of a person i’s foot position in camera
k, respectively. In order to get the more reliable speed of a
person, we average multiple speeds of the person along its
trajectory in each camera.
After calculating the speeds of people, we build a distance distribution based on person re-identification results
between two cameras. For example, we have a correspondence between two cameras {oki , olj }. We then estimate the
distance between two cameras by multiplying the speed and
the time difference as follows,

where N k and N l are the numbers of identities in camera
k and l. To find the relative scale between two cameras, we
find a scale ratio S that minimizes the following equation
k

2.0 m/s
∆𝑡=23

X

Figure 3. Examples of detected human heights in a 2D image and corresponding 3D human heights in a world coordinate system.

N X
N
X

CAM B

ID: 1 2.1 m/s
ID:8

CAM

Human height

Blind area

!
,

(3)

where H(oki ) is an average height of a person oki along its
moving path in the world coordinate system. Since the proposed Eq. (3) utilizes multiple correspondence pairs, we can
prevent overfitting the value S. We assume that every person is on the planar ground plane. Inspired by [25], we
can detect human foot and head points in a 2D image and
compute the corresponding person’s height according to the
projection matrix P as shown in Fig. 3.
After estimating the scale ratio S between two cameras,
the projection matrices of camera k and l are updated as


Pk = Kk Rk tk ,
(4)


Pl = Kl Rl S kl tl .

1 k
(v̄ + v̄jl ) · ∆t,
(6)
2 i
where ∆t is a transition time of the person who appears in
two cameras at different times. Note that it is impossible
to directly observe the speed of the person in the blind area
(i.e., area of between cameras). For that reason, to infer the
speed in the blind area, we average the two speeds (v̄ik and
v̄jl ) from two cameras. Figure 4 shows the example of the
distance estimation between cameras. Although the speeds
of two identities are different, the estimated distances are
the same. Using multiple correspondences between cameras, we make a histogram of the distance and normalize
the histogram by dividing with the number of the correspondences. We denote the distribution according to the distance
between two cameras k and l as pkl (d).
By performing distance distribution estimation between
all camera pairs in the camera network, we obtain the
distance-based camera network topology, and it is defined
by a graph as follows,
dkl =

Through the proposed process, we can adjust all extrinsic
camera parameters t in the camera network. Note that the
proposed framework does not aim to find the absolute scale
of the world coordinate system and real walking speeds of
people, but to match the scales across cameras.

3.3. Distance-based Camera Network Topology Inference

Gd = (V, Ed ) ,

To infer the distance-based camera network topology, we
exploit the speeds of people in each camera. We assume
that every person is on the planar ground plane (Z = 0,
world XY plane). Based on this assumption and camera
calibration result in Sec 3.2, the speed of a person oki is

V ∈ {k|1 ≤ k ≤ Ncam } ,

Ed ∈ pkl (d)|1 ≤ k ≤ Ncam , 1 ≤ l ≤ Ncam ,

(7)

where Ncam is the number of cameras in the camera network and V is a set of cameras and Ed is a set of distance
4

0.2

0

20

40

60

80

0

20

Transition time (sec)

(a) Transition time distribution

40

60

(b) Distance distribution

20

40

60

80

(a) Person speed: 2.1 m/s

0.4
0.2
0

20

40

60

80

Transition time (sec)

(b) Person speed: 0.8 m/s

Figure 6. Inferring adaptive transition time distribution for
each identity based on the speed of a person.
the fixed time range [20, 60] (sec) for all test queries as
shown in Fig. 5 (a). Although it has the much wider time
range (40 seconds) than our method, it may fail to find a
correct person who moves very slowly or fast. For example, its search range [20, 56.3) (sec) is redundant for the
person who moves slowly (0.8 m/s), and it fails to search
the person if the person reappears during (60, 73.8] (sec) in
the other camera.

distribution between cameras. As shown in Fig. 5, the variance of the distance distribution inferred by the proposed
method (Fig. 5 (b)) is smaller than that of the conventional
time-based transition distribution (Fig. 5 (a)). Note that it is
difficult to reduce the search range when the variance of the
distribution is large.

3.4. Person Re-identification via Distance-based
Camera Network Topology

Person re-identifiction. In this work, we utilize the LOMO
feature extraction method [19], which shows promising reidentification performance, to describe the appearances of
people. It divides a person image patch into six horizontal
stripes and extracts a HSV color histogram from each stripe.
It builds a descriptor based on Scale Invariant Local Ternary
Pattern (SILTP). The descriptor from the 128×48 (pixel)
image has 26,960 dimensions.
In general, each person gives multiple appearances along
with its trajectory. The multiple appearances provide rich
information for re-identification. However, a lot of computations are needed to take into account all the multiple appearances. Inspired by [46], we employ an average feature
pooling method. In average pooling, the feature vectors of
multiple appearances are pooled into one by the averaged
summation. Therefore, we can simply compare two identities and the similarity score between two identities is defined by

In this section, we restrict the search range based on the
inferred distance-based camera network topology and perform person re-identification.
Search range restriction.
Dividing the distance distribution pkl (d) by the speed of a person i in camera k gives a
transition time distribution of the person i who moves from
camera k to l as
pkl (d)
.
v̄ik

0.2

Transition time (sec)

Figure 5. Comparison of two distributions between cameras. The transition time distribution has a larger variance
than that of the distance distribution.

pkl
i (∆t) =

0.4

0

80

Distance (m)

0.6

𝑝(∆𝑡)

0.4

0.2

CAM2(Zone4) - CAM1(Zone4)

0.6

𝑝(∆𝑡)

0.4

0

CAM2(Zone4) - CAM1(Zone4)

CAM2(Zone4) - CAM1(Zone4)
0.6

𝑝(𝑑)

𝑝(∆𝑡)

CAM2(Zone4) - CAM1(Zone4)
0.6

(8)

Thus, we can adaptively give a camera network topology
for each person depending on its speed as shown in Fig. 6.
Based on the obtained transition time distribution pkl
i (∆t),
we restrict the search range for re-identification as follows:
• Find a mean value ofthe transition time distribution:
m = mean pkl
i (∆t) .


k
l
S oki , olj = e−kΦ(oi )−Φ(oj )k2 ,

• Set a search range Tr around the mean value to cover
95% of the distribution: [m − T2r , m + T2r ].

(9)

where Φ (·) is a pooled feature vector of a person. The similarity score lies on [0, 1]. Note that it is possible to utilize
any kind of feature extraction and pooling methods in our
framework.

In consequence, a person who moves fast (2.1 m/s) in the
camera k will be searched within the time range [21.4, 28.1]
(sec) in the camera l (Fig. 6 (a)). On the other hand, a
person who moves slowly (0.8 m/s) in the camera k will
be searched within the time range [56.3, 73.8] (sec) in the
camera l (Fig. 6 (b)). The search range of each person is
determined adaptively in this manner. Thus, the proposed
adaptive search strategy according to the walking speed of
a person becomes more effective under the large variation
of people’s speed.
On the other hand, a search strategy based on the conventional time-based camera network topology searches within

4. Dataset and Methodology
4.1. Dataset
Over the past few years, numerous datasets of person re-identification have been published such as VIPeR
[12], PRID 2011 [13], CUHK [17, 18], iLIDS-VID [37],
MARS [45] and Airport [15]. However, most of the
datasets do not provide camera synchronization information
5

2 3 C4
1 4

C6
4
6 5
2
1
3

1

C2
C1

3

5
4

2

1
3

the number of true matching results and Tgt is the total number of ground truth pairs in the camera network. In the SLP
dataset, Tgt = 2,664 as summarized in Table. 1. In practice,
rank-1 accuracy is the most important one among all ranks
since other ranks (2, 3, ..., n) failed to find the correct correspondences at least one time.
To evaluate the accuracy of the camera network topology inference, we draw a curve of the retrieval rate. The
retrieval rate represents the retrieval accuracy of matching
candidates derived from the camera network topology. For
example, if the matching candidates include the true correspondence of a test query, it counts as a success, otherwise
a fail. Naturally, when the topology gives a wide search
range Tr , the retrieval rate becomes high since the matching candidates within the wide search range are likely to include a true correspondence. For unbiased evaluations, we
draw a curve of the retrieval rate according to the average
search range as shown in Fig. 9. If there are multiple links
in the camera network, we measure the retrieval rates for all
links and average them to get the final rate. Since we have
no ground-truth of the camera network topology, measuring
the retrieval rate is reasonable.

5 1 2
4
3

3

C5
C3 C7
3 4
2 1

C8
2 3
1

2

C9
2
1

4
1
2

Figure 7. Layout of the SLP dataset. Each blue ellipse in
a camera is an entry–exit zone. Red lines are valid links
between cameras (best viewed in color).
Table 1. Numbers of transition identities (IDs) between two
cameras.
Link
1
2
3
4

CAM pairs # of IDs
CAM1-CAM2
227
CAM2-CAM3
571
CAM3-CAM5
568
CAM3-CAM7
168

Link
5
6
7
8

CAM pairs # of IDs
CAM4-CAM5
155
CAM5-CAM6
61
CAM7-CAM8
281
CAM8-CAM9
633

5. Experimental Results

or time stamps of all frames; thus these datasets cannot be
used for testing our framework.
To validate our methods and compare to other stateof-the-art methods, we used the SLP re-identification
dataset [6]. It is a large-scale person re-identification dataset
containing nine synchronized outdoor cameras. The total
number of identities in the dataset is 2,632. The layout of
the camera network is shown in Fig. 7. It has eight valid
links between cameras as summarized in Table. 1. It provides the ground truth detection and tracking information
of every person including people positions (x,y locations)
and sizes (height, width). We used the given detection and
tracking results for our experiments, since we mainly focus
on person re-identification and camera network topology inference problems. Most person re-identification researches
follow this assumption and setting. We resized the every
person image to 128×48 pixels and extracted feature descriptors from the resized images.

In the experiments, we assume that the camera parameters are partially given – camera intrinsic parameter K and
camera rotation R are given, but the camera translation is
given by S · t, where S is unknown. Thus, the cameras in
the camera network do not share the same world coordinate
system, initially. To identify a pair of cameras is connected
or not, we applied the connectivity check schemes in [6, 26]
and we obtained initial person re-identification results based
on [6] for our framework.
Using the initial re-identification results, we perform
two steps in our framework: 1) relative scale estimation
between cameras, and 2) distance-based camera network
topology inference. However, the initial results of the reidentification between cameras may include several false
correspondences leading an inaccurate topology inference
result. Therefore, we utilized only reliable people corre
spondences that have high similarity scores: S oki , olj >
0.7 and excluded the false correspondences in both steps 1)
and 2). We empirically set the threshold as 0.7, but our
framework does not highly depend on this threshold. As a
result, we found seven links in the camera network. Unfortunately, we failed to find a link CAM5-CAM6 since the
number of people is small due to the long distance from the
camera (Fig. 7). Also, CAM6 is isolated from other cameras
as shown in Table. 1.
Figure 8 shows comparisons of two types of distribution
between cameras: (left) transition time distributions in conventional time-based topology, (right) distance distributions
in proposed distance-based topology. As we can see, the

4.2. Evaluation methodology
To evaluate a person re-identification performance, many
previous works plot a Cumulative Match Curve (CMC) [12]
representing true match being found within the first n ranks.
In general, to plot the CMC, the number of a gallery (i.e.,
matching candidates) should be fixed for all test queries.
However, in our framework, the number of a gallery varies
according to the camera network topology and test queries.
Therefore, we cannot plot a complete CMC. In this work,
we followed Cho’s [6] re-identification evaluation metric:
P
measuring the rank-1 accuracy by 100 · TTgt
, where T P is
6

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𝑝(𝑑)
0

20

40

0

60 (sec)

𝑝(𝑑)
0

20

40

0

60 (sec)

0.4

0.4

𝑝(𝑑)

0.6

0.8

Distance-based (gt)
Distance-based (ours)
Time-based
Distance-based (error 25%)
Distance-based (error 50%)

0.7
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0

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0

10

20
30
40
Average search range (sec)

50

60

(m)

Figure 9. Topology accuracy: retrieval rate according to the
average search range.
0

20

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80

(m)

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𝑝(𝑑)

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(m)

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Distance-based (gt)
Distance-based (ours)
Time-based
Distance-based (error 25%)
Distance-based (error 50%)

50

30
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𝑝(𝑑)

𝑝(∆𝑡)

(m)

0.9

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60

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50

60

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Figure 10. Re-identification accuracy: rank-1 accuracy according to the average search range.

0.2

0.2
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40

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Rank1 accuracy

𝑝(∆𝑡)

CAM2-CAM3

0

60 (sec)

0.6

𝑝(∆𝑡)

CAM3-CAM5

40

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𝑝(∆𝑡)

CAM4-CAM5

20

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0.6

𝑝(𝑑)

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CAM1-CAM2

1
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(a) Transition time distribution

0

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(m)

To validate the effectiveness of the proposed method, we
first evaluated the topology inference accuracy: retrieval
rate according to the average search range. In this experiment, we compared a distance-based method with a timebased method. For a fair comparison, we applied the same
baseline (e.g., feature extraction and pooling methods) to
each method. As shown in Fig. 9, the proposed distancebased method (ours) shows superior performance than the
conventional time-based method. To verify the proposed
scale estimation method in Sec. 3.2, we also compared several distance-based methods with various experimental settings: • gt: a method using ground-truth camera parameters for all cameras, • error N%: a method using camera parameters with N percent of scale error. As we can
see, distance-based methods with erroneous camera parameters (error 25%, 50%) show lower performance than the
distance-based method with ground-truth camera parameters (gt). On the other hand, the proposed method (ours)
shows a similar performance with the distance-based (gt).
Interestingly, our method shows higher performance than
distance-based (gt) around 5–10 second search times. In addition, a distance-based method (error 25%) shows better a
retrieval rate than that of the time-based method. It implies
that our method is robust to camera calibration error.
We also evaluated the accuracy of re-identification based
on each camera network topology. As shown in Fig. 10,
the proposed distance-based method (ours) shows superior

(b) Distance distribution

Figure 8. Comparisons of two types of inferred distributions.
proposed distance distributions show more clear peaks and
small variances compared to those of transition time distributions. The result implies that the proposed distancebased topology is more effective than the time-based topology for person re-identification, since it is ambiguous to restrict search range with the unclear camera network topology. A CAM3-CAM5 pair has the negative values of both
transition time and distance since they are overlapped.
Ideally, a distance between two cameras should be one
value if there is a single path between cameras. However,
the proposed distance distributions did not converge to one
value, but have some ranges e.g., CAM1-CAM2: [40, 55]
and CAM4-CAM5: [35, 55]. This is because the speeds and
paths of moving people in the blind region are totally unknown and can differ person to person. In addition, other
values, which are used for inferring the topology, such as
camera parameters and observed people positions in each
view are not perfect due to noise. To overcome these limitations, we estimated the distance of the blind area by interpolating the information on both sides of cameras as in
Eq. (6) and could obtain quite clear distance distributions.
7

Table 2. Performance comparison with state-of-the-art methods.
Methods Makris’s [26] Nui’s [29] Chen’s [5] DNPR [27] Cai’s [3] Cho’s [6] ours–time ours–dist
rank-1 accuracy
54.0
54.6
55.2
44.7
51.4
68.3
67.8
74.7
0.07

CAM3
CAM7

0.25

0.06

CAM7
CAM8

1

1

1

CAM8
CAM9

0.8
0.8

0.2

0.8

0.04

0.15

0.6

0.03

0.1

0.4

0.4
0.4

0.05

0.2

0.2
0.2

0.05

0.6
0.6

0.8
0.6

0.5

y

0.02
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6060

8080

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2.5

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(e) ours–dist

Figure 11. Comparison of inferred transition time distributions of previous methods and our distance distribution. First row
– camera pair: CAM3-CAM7. Second row – camera pair: CAM7-CAM8. Third row – camera pair: CAM8-CAM9.
re-identification performance than that of the time-based
method. All tested re-identification performances increase
up to a certain search range and then decrease. This is because a wide search range retrieves a lot of matching candidates that are likely to include a true correspondence, but
they also include lots of irrelevant identities. Thus, it is important to set a proper search range. In our methods, we
searches in the range of 20 seconds on average based on
the propose search range restriction in Sec. 3.4. It is reasonable for both topology and re-identification performances as
shown in Fig. 9 and 10.
We compared the proposed method with several previous
methods [3, 5, 6, 26, 27, 29] that infer a time-based camera
network topology. Figure 11 shows comparison of inferred
transition time distributions and an inferred our distance
distribution. As we can see, the methods [5, 26, 29] show
very unclear and noisy transition time distributions. This is
because, to infer the topology, they used a simple correlation of people exiting–entering patterns instead of utilizing
re-identification results. On the other hand, the method [6],
which used re-identification results to infer the topology,
shows reasonable results than other methods [5, 26, 29].
Compared to [6], our distance distributions between camera pairs show more clear peaks and have small variances.
In Table. 2, we summarize the person re-identification
results based on each inferred camera network topology. In
this experiment, we have two results: ours–time and ours–
dist. They share the same baseline except for the utilized
camera network topology (time-, and distance-based) for

re-identification. For a fair comparison, we set the same
average search range (20 seconds) for both of our methods.
Among the methods, which utilized the time-based camera
network topology, the method [6] showed the highest reidentification performance. It employed the random forest
algorithm [2] to perform accurate person re-identification.
On the other hand, our methods (ours–time, ours–dist) used
a simple feature pooling method for re-identification as described in Sec. 3.4. Although our method employed the
simpler re-identification method, re-identification using the
proposed distance-based topology shows superior performance than other state-of-the-art methods.

6. Conclusions
In this paper, we proposed a novel distance-based camera network topology inference. We first estimate relative
scale ratio between cameras based on the human heights
information and infer the distance-based camera network
topology. The proposed distance-based topology can be
applied adaptively to each person according to its speed;
therefore it can effectively handle the various people transition time between cameras. In order to validate the proposed method, we used a public synchronized large-scale
re-identification dataset and compared our method with
state-of-the-art methods. The results show that the proposed
method is promising for person re-identification in largescale camera network with various people transition time
between cameras.
8

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