Feature Extraction using Downsamplingfor Person Re-identification with Low-Resolution Images
Masashi Nishiyamaa, Takuya Endo and Yoshio Iwaib

Graduate School of Engineering, Tottori University, Tottori, Japannishiyama@tottori-u.ac.jp

Keywords:

Person re-identification, Low-resolution images, Low-frequency components, Downsampling.

Abstract:

We investigate whether a downsampling process of high-resolution pedestrian images can improve person reidentification accuracy. Generally, deep-learning and machine-learning techniques are used to extract featuresthat are unaffected by image resolution. However, it requires a large number of pairs of high- and lowresolution images acquired from the same person. Here, we consider a situation in which these resolution
pairs cannot be collected. We extract features from low-resolution pedestrian images using only a simple
downsampling process that requires no training resolution pairs. We collected image resolution datasets bychanging the focal length of the camera lens and the distance from the person to the camera. We confirmed thatthe person re-identification accuracy of the downsampling process was superior to that of the upsampling. We
also confirmed that the low-frequency components corresponding to the output of the downsampling processcontain many discriminative features.

1

INTRODUCTION

There is a need for a person re-identification system that covers wide areas using surveillance cameras installed at various locations to track people.
This system is expected to search for the routes oflost children and suspicious persons to make society
safe and secure. The key task in designing a person re-identification system is to determine how to
extract the features that represent the personal characteristics from pedestrian images. To do this, various existing methods (Zhong et al., 2017; Zheng
et al., 2015) have been proposed. When extracting
features, existing methods perform better if they acquire high-resolution (HR) pedestrian images containing many personal characteristics. In general, HR
images increase the probability of successful identification. However, the resolution of pedestrian images obtained from surveillance cameras is not always
high; it varies dynamically depending on the camera
settings, such as the camera lens and camera position.
When pedestrian images are acquired at low resolution, it becomes difficult to stably extract features that
correctly represent the characteristics of the individuals. This causes the person re-identification accuracy
to decrease.
To overcome the problem caused by lowresolution (LR) pedestrian images, existing methods (Jing et al., 2015; Wang et al., 2016; Zhengab

https://orcid.org/0000-0002-5964-3209
https://orcid.org/0000-0003-2248-4563

et al., 2018; Jiao et al., 2018) take into account resolution variation at the feature extraction process for
person re-identification. These existing methods use
a learning-based approach in which pairs of HR and
LR images of the same person are used as training samples for deep-learning and machine-learningtechniques. They promise to improve person reidentification accuracy by extracting features that areless affected by the resolution variation. However,the learning-based approach frequently requires the
preparation of a large number of HR and LR image
pairs, and their collection is very labor-intensive.
Our challenge is to determine whether the accuracy of person re-identification can be improved when
there are no HR and LR training image pairs of thesame individual. Instead of a learning-based approachthat requires a large number of training samples, we
exploit a resampling approach. Our approach has theadvantage that it can be applied in cases where training samples consisting of pairs of HR and LR images
cannot be collected. There are two types of resampling processes: upsampling to match the size of the
HR target images and downsampling to match the sizeof the LR query images. We experimentally demonstrated which process is more suitable for improving person re-identification accuracy. We confirmedthat the downsampling process obtained significantly
higher person re-identification accuracy than the upsampling process. We also confirmed that the lowfrequency components corresponding to the output ofthe downsampling process contain informative features on the publicly available CUHK01 dataset.

2

Related work

We first review a survey paper (Wang et al., 2014) focusing on image resolution in the field of face recognition, where the purpose is relevant to our paper. The
survey paper states that two approaches exist to deal
with face recognition problems caused by low resolution. In the first approach (Shi and Jain, 2019), a feature space that is less affected by resolution variation
is designed, and the recognition process is performedin that feature space. In the second approach (Zou andYuen, 2011), LR images are converted to HR images,
and the recognition process is performed in an HRfeature space. In especial, some researchers (Aghdam et al., 2019; Hennings-Yeomans et al., 2008) discussed the suitable resolutions for face recognition.
However, these related studies did not treat the caseof person re-identification. Recently, several existing
methods employing the first and second approaches
have also been proposed for person re-identification.
Using the first approach, the existing methods (Jing et al., 2015; Wang et al., 2016) have been
proposed to design a feature space that is less sensitive
to resolution variation for person re-identification.
They exploit learning-based methods to design feature spaces that are not easily affected by resolution
variations using deep-learning or machine-learningtechniques with pairs of HR and LR images of thesame person. Using the second approach for person re-identification, the existing methods (Zheng
et al., 2018; Jiao et al., 2018) have been proposed.
To convert LR images to HR images, they exploit
super-resolution techniques, which incorporate deeplearning models trained from the same person’s resolution image pairs.
In both approaches, if we prepare a large number of training samples of resolution image pairs,
we can expect to increase the accuracy of the person re-identification substantially. However, collecting a large number of training samples of resolution
image pairs is very laborious and costly. In this paper, we aim to improve the accuracy using a resampling approach in which only preprocessing is applied
to pedestrian images instead of a learning-based approach in which training samples are required.

3

Effect of image downsampling

3.1

Image resampling methods

The resolution of pedestrian images in person reidentification systems varies in both the query samples and target samples. To simplify the problem

setup, we assume that HR pedestrian images are given
to target samples and LR pedestrian images are given
to query samples. Under this assumption, we evaluate whether the pedestrians in the query and target
samples are identical or not. We compare person
re-identification accuracy using the following two resampling processes:• Cd : We perform downsampling on the HR target
images to match the size of the LR query images.
Then, we identify the original LR query images
using the downsampled HR target images.
• Cu : We perform upsampling on the LR query images to match the size of the HR target images.
Then, we identify the upsampled LR query images using the original HR target images.
We use bilinear interpolation for Cd and a superresolution convolutional neural network (SRCNN) (Dong et al., 2015) for Cu .

3.2

Factors reducing the resolution ofpedestrian images

To evaluate the person re-identification accuracy using the resampling process, it is necessary to collecta dataset of pedestrian images consisting of queryand target samples. To determine a dataset collection
strategy, we considered the factors that cause a decrease in resolution in various camera settings. Typical factors are represented by the parameter changes
in the camera lens and camera positions. Each factor
is described in detail below.
We consider the influence of the camera lens on
the resolution. The performance of camera sensors
has improved remarkably in recent decades, and the
majority of camera sensors have a large number ofpixels. Thus, images acquired from cameras usually
have high resolution. However, a single surveillancecamera covers a wide field of view, and the resolutionof an image of an individual pedestrian is often low.
Furthermore, the resolution of the pedestrian image
decreases when the focal length of the camera lensis small, even if the number of pixels of the camera
sensor is large. For example, the resolution becomes
low when a wide-angle lens with a short focal length
is attached.
Next, we consider the influence of the camera position on resolution. The position of a camera varies
depending on the purpose of the surveillance systems.
For example, to monitor a specific point, such as thedoor to a train, the cameras are placed close to passengers who are entering the train. To monitor a large
space, such as an airport lobby, the cameras are placedso that they have a wide view that is far away from

Focal length:
135 mm – 18 mm

Height: 167.3 cm

Height:167.3 cmDistance:
0m

5m

10 m

15 m

20 m

25 m

Distance: 15 m
Location1

Location1

Location2

Location2

5 m 10 m 15 m 20 m 25 m
Distance
Person 1

135 mm 70 mm 35 mm 18 mm
Focal length
Person 1
Location1

Location1

Location2

Location2
135 mm 70 mm 35 mm 18 mm
Focal length
Person 2

Figure 1: Camera setting when the focal length of the camera lens is changed.

people. Assuming that the camera lenses are identical, the resolution is high when the distance between
the camera and the person is small. In contrast, theresolution is low when this distance is large.
In the following, we describe two sets for evaluating person re-identification accuracy: one in which
the focal length of the camera lens changes and another in which the distance between the person andcamera changes.

3.3

3.3.1

Data collection of LR pedestrian
images
Focal length change dataset

Figure 1 shows the camera setting used when the
focal length of the camera lens was changed. We
set the focal length to 135 mm, 70 mm, 35 mm,
and 18 mm and acquired query pedestrian images foreach setting. The average sizes of the pedestrian im-

5 m 10 m 15 m 20 m 25 m
Distance
Person 2

Figure 2: Camera setting when the camera–person distanceis changed.

ages were (58.3, 186.3), (31.3, 100.5), (15.4, 49.5),
and (7.2, 24.2) pixels, respectively. We used a focal length of 135 mm, to acquire the target pedestrian images at a different time. The camera (Sonyα6300 with E 18-135mm F3.5-5.6 OSS) was placed
at a height of 167.3 cm above the floor. The distancefrom the person to the camera was fixed at 15 m. The
images were acquired at five different indoor locations unaffected by ambient light. Twenty subjects(17 men and three women) participated.
3.3.2

Distance change dataset

Figure 2 shows the camera setting used when the distance from the camera to the person was changed.
We set the distance to 5 m, 10 m, 15 m, 20 m, and
25 m and again acquired the query pedestrian images
for each setting. The average sizes of the pedestrian
images were (59.2, 192.9), (29.2, 94.7), (19.6, 62.4),(14.3, 46.7), and (11.7, 37.1) pixels, respectively. We
used a distance of 5 m to acquire the target pedestrian
images at a different time. The camera (Logicool HD

100

Pro Webcam C920r) was placed at a height of 167.3
cm above the floor. To keep the lighting on the person constant, we fixed the person’s standing position
and moved the camera position. The images were acquired at five different indoor locations. Twenty subjects (17 men and three women) participated.

Accuracy (%)

Accuracy of person re-identification

We performed the downsampling Cd and upsampling
Cu described in Section 3.1 to pedestrian images
as preprocessing before feature extraction. We extracted features using co-occurrence attributes proposed by (Nishiyama et al., 2016). Co-occurrence
attributes are represented by combinations of physical and adhered human characteristics (e.g., a man
wearing a suit, a 20-something woman, or a woman
with long hair who is wearing a skirt). To increase theaccuracy, we applied the large margin nearest neighbor (LMNN) (Weinberger and Saul, 2009), which isa kind of metric learning technique. We used the
CUHK01 dataset (Li et al., 2012) as training samples only for the metric learning. We applied thenearest neighbor approach using Euclidean distanceto compute distances between the query and target
features. We used the first matching rate (Rank-1),
which measures a correct hit between the person in
the query sample and one of the target samples. We
computed the average and the standard deviation of
first matching rate from the five locations included ineach dataset.
We evaluated the person re-identification accuracy
on the dataset with varying camera lens focal lengths.
Figure 3 shows the average and the standard deviation of first matching rate of person re-identification
when Cd and Cu were applied in the focal length
dataset. We see that Cd , which downsamples to thesize of the LR query samples, improves the person
re-identification accuracy better than Cu , which upsamples to the size of the HR target samples.
Next, we evaluated the person re-identification accuracy on the dataset with varying distances from theperson to the camera. The experimental conditions
were the same as those in the above evaluation except for the dataset. Figure 4 shows the average andthe standard deviation of first matching rate of person
re-identification when Cd and Cu were applied in the
distance change dataset. Similar to the above results,
we see that downsampling Cd obtains better accuracy
than Cu .
We believe that downsampling Cd is more suitable as a preprocessing technique for person reidentification than upsampling Cu in both the datasets,
where the resolution is reduced by the focal length of

60
40
20
0

C𝑑 (Downsampling)C𝑢 (Upsampling)
18
35
70
135
Focal length of the camera lens (mm)Figure 3: Accuracy of person re-identification when changing the focal length of the camera lens.
100

80
Accuracy (%)

3.4

80

60
40
20
0

C𝑑 (Downsampling)C𝑢 (Upsampling)
5
10
15
20
25
Distance from the camera to the person (m)Figure 4: Accuracy of person re-identification when changing the camera–person distance.

the camera lens and the distance from the camera to
the person.

4

Analysis of downsampling on
person re-identification

4.1

Overview

The downsampling process improves the person reidentification accuracy more than the upsampling process, as described in Section 3.4. Here, we use
spatial frequency analysis to further investigate why
the downsampling process increases the accuracy.
We assume that an HR image can be separated into
low-frequency components and high-frequency components. In particular, we consider that the lowfrequency components of an HR image correspondto an LR image. In this experiment, we investigate
how person re-identification accuracy varies when using the following three images:
Fa : Images containing all frequency components.
Fl : Images containing low-frequency components.

Original
images

"

𝒇!

"

𝒇#

𝒈"↑

𝒈"↑
!

𝒈"↑
#

𝒇

"

SRCNN

Figure 5: Visualization of pedestrian images consistingof high-frequency and low-frequency components on the
CUHK01 dataset.

Fh : Images containing high-frequency components.
We use pseudo HR images upsampled from LR images with the SRCNN as the query samples. The
target samples are the original HR images. We consider that Fa corresponds to Cu with upsampling in
Section 3.1, and Fl corresponds to Cd with downsampling.

4.2

Experimental conditions

We performed image degradation simulations usinga model where HR pedestrian images were used tocreate various LR images. This model synthesized theLR query samples g q using the following equation:g q = BH f q + n(1)

where B is the downsampling process, H is the blur
process, f q is the original HR query samples, and n is
noise. We assumed that B and H are shift-invariant.
Specifically, B represents bilinear interpolation, H
Gaussian blur, and n white Gaussian noise.
We used the CUHK01 dataset (Li et al., 2012),
consisting of 971 pedestrians. We assigned pedestrian images acquired at different times to the queryand target. We evaluated 971 query samples and 971
target samples. The size of all pedestrian images was
normalized to (60, 160) pixels. To generate the LR
query samples g q using Eq. (1), we set the image sizes
to (30, 80), (24, 64), (18, 48), (12, 32), and (6, 16)
pixels. We also generated upsampled LR query samples g q↑ with a size of (60, 160) pixels from g q using the SRCNN. The size of the target samples was(60, 160) pixels.
We separated g q↑ into high-frequency and lowfrequency components in the frequency domain using

a discrete Fourier transform. Then, each component
was transformed into an image in the spatial domainusing an inverse discrete Fourier transform. We set
the boundary between the low-frequency and highfrequency components to match the resolution of eachq↑pedestrian image. We created images g h containing
only high-frequency components computed from g q↑q↑
and images g l containing only low-frequency comq
ponents. We also created images f h containing only
high-frequency components of the original HR imq
age f q and images f l containing only low-frequency
components.
Figure 5 shows a visualization of the highfrequency and low-frequency component images. We
used a size of f q and g q↑ with (60, 160) pixels. Imageg q↑ was upsampled from g q with (6, 16) pixels. We
mapped the range of pixel values from [−128, 128] to[0, 255] to visualize high-frequency components because the high-frequency components can take positive or negative values. When we focus on the highfrequency components, we see that the edges of theface and the pattern of the clothing occur in the highq
frequency components f h of the original images. In
contrast, some weak edges occur around the pedesq↑
trian’s contour in the high-frequency images g h upsampled using the SRCNN. We also see that the appearances of the pedestrians are similar in the lowq
frequency image f l computed from the original imq↑
age and the low-frequency image g l computed fromg q↑ upsampled using the SRCNN.

4.3

Accuracy of person re-identificationon the CUHK01 dataset

To perform person re-identification, we used the following methods:• CA: LMNN with the co-occurrence attributes described in (Nishiyama et al., 2016) (We explained
the CA method in Section 3.4).
• SL: CNN with softmax cross entropy loss withlabel smoothing regularizer described in (Szegedy
et al., 2016; Szegedy et al., 2016).
• TL: CNN with triplet loss described in (Hermans
et al., 2017).
• OS: Omni-Scale Network (OSNet) describedin (Zhou et al., 2019).
We used 486 randomly selected pedestrians for training the LMNN metric matrix of the CA method fromthe CUHK01 dataset. The remaining 485 pedestriansin this dataset were used for the query and target samples of the CA method. We used the same pedestrian
images employed in the CA method for the query and4.4

Evaluation of different
super-resolution techniques

We evaluated the person re-identification accuracy using existing super-resolution techniques SRCNN (Dong et al., 2015), ESRGAN (Wang et al.,2018), and USRNet (Zhang et al., 2020) as upsampling processes. We also evaluated it using basic bilinear and bicubic interpolation techniques. The experimental conditions are the same as those describedin Section 4.3 except for the upsampling techniques.

True positive rate

10

━ F! (AUC = 0.843)━ F" (AUC = 0.982)━ F# (AUC = 0.507)
0False positive rate1(a) CA
1

True positive rate

target samples of the the SL, TL, and OS methods.
The backbone network of the SL and TL methods was
ResNet50 (He et al., 2016). We used the Torchreid
implementation (Zhou and Xiang, 2019) with training samples of the Market-1501 dataset (Zheng et al.,2015) for the SL, TL, and OS methods.
Table 1 shows the person re-identification accuracy using the CA, SL, TL, and OS methods on the
CUHK01 dataset. We see that Fl with low-frequencyq↑component images g l is superior to Fa with g q↑ andq↑
Fh with gh for all the methods. In the case of Fh with
only high-frequency component images, no accuracy
could be obtained at all. Figure 6 shows the receiver
operating characteristic (ROC) curves when using theFa , Fl , and Fh conditions with the CA and OS methods. We used g q↑ upsampled from g q with (18, 48)
pixels. We see that Fl is superior to Fa and Fh according to the ROC curves for both the CA and OS methods. We believe that the low-frequency components
corresponding to the output of the downsampling process yields promising results regardless of the designof the person re-identification method.
Furthermore, we also checked the performance ofthe downsampling Cd with bilinear interpolation andupsampling Cu with SRCNN, as described in Section 3.1. We used g q in Cd and g q↑ in Cu . In thecase of the CA method, the accuracy of Cd were
76.2% with (30, 80) pixels, 75.1% with (24, 64) pixels, 74.5% with (18, 48) pixels, 70.4% with (12, 32)
pixels, and 56.6% with (6, 16) pixels. Those of Cu
were 70.9%, 70.4%, 11.9%, 2.6%, and 0.5%, respectively. Next, in the case of the OS method,
the accuracy of Cd were 93.5% with (30, 80) pixels, 89.7% with (24, 64) pixels, 72.6% with (18, 48)
pixels, 34.0% with (12, 32) pixels, and 25.7% with
(6, 16) pixels. Those of Cu were 78.7%, 76.8%,
69.7%, 52.3%, and 19.0%, respectively. We confirmed that downsampling Cd obtains significantly
better accuracy than upsampling Cu on the CUHK01
dataset. We believe the effectiveness of downsampling appears in this experiment as well as in the results in Section 3.4.

0

━ F! (AUC = 0.978)━ F" (AUC = 0.991)━ F# (AUC = 0.513)
0False positive rate1(b) OS

Figure 6: ROC curves when using the Fa , Fl , and Fh conditions on the CUHK01 dataset. We checked the area under
the curve (AUC).

To perform person re-identification, we used the OS
method. Table 2 shows the person re-identification
accuracy when using the bilinear, bicubic, SRCNN,ESRGAN, and USRNet techniques. We see that
Fl obtained a higher accuracy than Fa and Fh regardless of which image interpolation technique wasused. We believe that informative features for person
re-identification are contained in the low-frequency
components. Since Fl provides the same effect as
Cd , we again confirmed the downsampling process
obtains better accuracy than the upsampling process,even when using super-resolution techniques and basic interpolation techniques.

5

Conclusions

We investigated whether the downsampling process,which does not require training samples of HR and
LR image pairs, obtains better person re-identification

Table 1: Comparison of the accuracy of the Fa , Fl , and Fh methods when using the person re-identification methods CA, SL,
TL, and OS on the CUHK01 dataset.
Image sizeof g q(30, 80)(24, 64)(18, 48)(12, 32)(6, 16)

Feature
CA
SL
TL
OS
CA
SL
TL
OS
CA
SL
TL
OS
CA
SL
TL
OS
CA
SL
TL
OS

Fa with g q↑
70.9 ± 1.7
62.8 ± 1.0
49.6 ± 1.9
78.8 ± 1.0
70.4 ± 1.2
61.4 ± 0.7
48.0 ± 2.6
77.4 ± 1.5
11.9 ± 1.2
53.7 ± 0.5
44.1 ± 1.9
71.3 ± 2.1
2.6 ± 0.4
41.0 ± 0.7
37.2 ± 1.5
53.1 ± 0.5
0.6 ± 0.2
17.5 ± 0.4
26.8 ± 1.1
19.3 ± 0.7

accuracy than upsampling. We collected datasets in
two camera settings to evaluate the influence of resolution variations by changing the focal length of thecamera lens and the distance from the camera to theperson. The experimental results show that downsampling to match the size of LR images contributes
significantly to obtaining high person re-identification
accuracy, in contrast to upsampling to match thesize of HR images. We also compared the accuracy
when using high-frequency and low-frequency components extracted from the upsampled LR images insimulation experiments. We confirmed that the lowfrequency components, which correspond to downsampled images, are likely to contain many informative features.
In future work, we intend to develop a resampling method for further increasing person reidentification accuracy and expand the evaluation
on various datasets containing person appearanceschanges. We also need to analyze the relevance of
our findings to the image generation process, such as
optical systems in real environments.

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Accuracy (%)
q↑
Fl with g l
73.3 ± 0.4
65.2 ± 0.7
52.3 ± 1.4
88.2 ± 0.4
73.7 ± 0.3
64.3 ± 1.9
54.1 ± 1.6
87.7 ± 0.3
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57.1 ± 0.8
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64.1 ± 0.8
60.6 ± 1.5q↑

Fh with g h
0.2 ± 0.0
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0.3± 0.1
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0.2 ± 0.0
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0.2 ± 0.0
0.2 ± 0.0
0.2 ± 0.0
0.2 ± 0.0
0.2 ± 0.0
0.4 ± 0.2
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Table 2: Comparison of the accuracy of the Fa , Fl , and Fh conditions using super-resolution techniques (SRCNN, ESRGAN,
and USRNet) and basic interpolation techniques (Bilinear and Bicubic) on the CUHK01 dataset.
Image sizeqof g a(30, 80)(24, 64)(18, 48)(12, 32)(6, 16)

Interpolation
technique
BilinearBicubic
SRCNN
ESRGAN
USRNetBilinearBicubic
SRCNN
ESRGAN
USRNetBilinearBicubic
SRCNN
ESRGAN
USRNetBilinearBicubic
SRCNN
ESRGAN
USRNetBilinearBicubic
SRCNN
ESRGAN
USRNetq↑

Fa with g a
76.8 ± 1.6
75.8 ± 1.6
78.8 ± 1.0
78.1 ± 1.2
77.5 ± 1.5
70.4 ± 2.3
69.8 ± 2.6
77.4 ± 1.575.9 ± 0.8
73.7 ± 2.3
53.2 ± 2.5
55.8 ± 1.6
71.3 ± 2.1
69.3 ± 0.4
59.4 ± 1.1
29.4 ± 1.6
31.0 ± 1.6
53.1 ± 0.5
62.7 ± 1.1
32.5 ± 0.2
7.6 ± 0.1
8.1 ± 1.2
19.3 ± 0.7
36.8 ± 1.8
7.8 ± 0.7

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Accuracy (%)
q↑
Fl with g l
86.0 ± 0.9
85.6 ± 1.5
88.2 ± 0.4
88.5 ± 0.6
86.8 ± 1.3
80.5 ± 0.8
82.4 ± 0.4
87.7 ± 0.3
88.0 ± 0.3
84.1 ± 0.0
61.7 ± 1.2
69.1 ± 1.1
83.3 ± 0.6
85.9 ± 0.7
71.9 ± 0.1
33.1 ± 1.5
42.3 ± 1.7
70.8 ± 0.9
81.1± 0.2
45.5 ± 1.0
18.2 ± 0.5
27.8 ± 0.3
60.6 ± 1.5
57.6 ± 1.2
30.8 ± 1.4

q↑

Fh with g h
0.2 ± 0.0
0.3 ± 0.1
0.2 ± 0.1
0.4± 0.0
0.3 ± 0.1
0.2 ± 0.0
0.3 ± 0.1
0.2 ± 0.0
0.1 ± 0.1
0.3 ± 0.0
0.2 ± 0.0
0.2 ± 0.0
0.3 ± 0.0
0.5 ± 0.1
0.2 ± 0.0
0.2 ± 0.0
0.2 ± 0.0
0.2 ± 0.0
1.3 ± 0.2
0.2 ± 0.0
0.2 ± 0.0
0.2 ± 0.0
0.3 ± 0.1
1.3 ± 0.2
0.3 ± 0.1

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