Gait Collector: An Automatic Gait Data Collection System
in Conjunction with an Experience-based Long-run Exhibition
Yasushi Makihara, Takuhiro Kimura, Fumio Okura, Ikuhisa Mitsugami, Masataka Niwa,
Chihiro Aoki, Atsuyuki Suzuki, Daigo Muramatsu, Yasushi Yagi
The Institute of Scientific and Industrial Research, Osaka University
8-1 Mihogaoka, Ibaraki, Osaka, 567-0047, JAPAN{makihara,kimura,okura,mitsugami,niwa,c-aoki,a-suzuki,muramatsu,yagi}@am.sanken.osaka-u.ac.jp

Abstract

Operator fortreadmill

Biometric data collection is an important first step toward biometrics research practice, although it is a considerably laborious task, particularly for behavioral biometrics such as gait. We therefore propose an automatic gait
data collection system in conjunction with an experiencebased exhibition. In the exhibition, participants enjoy an
attractive online demonstration of state-of-the-art videobased gait analysis comprising intuitive gait feature measurement and gait-based age estimation while we simultaneously collect their gait data along with informed consent.
At the time of this publication, we are holding the exhibitionin association with a science museum and have successfullycollected the gait data of 47,615 subjects over 246 days,
which has already exceeded the size of the largest existing
gait database in the world.

1. Introduction
Gait recognition [26] is a behavioral biometric that isavailable even at a distance from the camera, when otherbiometrics may be occluded, obscured, or suffer from insufficient image resolution (e.g., a face that is blurred or
occluded by a mask). Because gait recognition does not require subject cooperation thanks to its non-invasive capture
process, it is expected to be applied to criminal investigation from CCTV footage in public and private spaces. In
fact, there is already research on using gait biometrics as aforensic tool [3, 7, 14].
Gait recognition studies involve a variety of stages:
data collection, preprocessing, feature extraction, matching, and system development. Among these, the data collection stage is increasingly important because of the recent rapid progress of machine learning techniques. Robust approaches to gait recognition must be robust against
covariates such as view, speed, clothing, and carrying sta-

Operator for
emergency stop button

Subject

Operator fordata capturing

Figure 1: Data collection experiment setup for the OU-ISIR
Treadmill dataset [15], where three operators are needed to
run the system.

tus, and indeed, the most robust approaches to gait recognition, adopt machine learning techniques, e.g., matrix factorization [19, 25], manifold learning [1, 22], subspace learning [11, 13], discriminant analysis [2, 23], support vector
machines (SVMs) [10, 24], and neural networks [9]. In addition, a large-scale gait database is essential for the statistically reliable performance evaluation of gait recognition [8]as well as the construction of a reliable probability density
function of genuine subjects and imposters for forensic purposes [7].
Gait data collection at a large scale is an extremely laborious and costly task, because gait data are essentially composed of videos, and hence both data collectors and subjects
need to be engaged in data collection experiments for long
periods of time, compared with other biometrics obtained
from still images (e.g., fingerprints, irises, and faces).
To gain a deeper understanding of this issue, imagine
the data collection process for a certain gait database, forexample, the OU-ISIR Treadmill dataset [15]. First, subjects must be found (e.g., volunteers or part-time workers)
through advertisement. Second, the aim of data collection
experiments must be explained and informed consent ob-

978-1-5090-1869-7/16/$31.00 ©2016 IEEEtained (e.g., a signed release agreement) from the subjects.
After the subjects have sufficiently practiced walking on thetreadmill, their gait data may be collected with the cooperation of multiple operators for the treadmill, video capture
software, and emergency stop button (see Fig. 1). It is therefore difficult to collect a large number of subjects with thiskind of setup.
To overcome this difficulty, collecting gait data in conjunction with an experience-based exhibition of videobased gait analysis is an option, where a large number ofparticipants visit, even without any explicit calls for subjects(The terms participant and subject are used interchangeably from the viewpoints of the experience-based exhibition and gait data collection in this paper.). In fact, theworld’s largest gait database, the OU-ISIR Large Population dataset [8], which contains more than 4,000 subjects,
was constructed through such exhibitions, i.e., several-day
exhibitions in a science museum or on an open campus.
However, this framework does not solve the other aspect:substantial personnel are still required to operate the gait
capturing software, check the baggage of the participants,
and annotate the result of video-based gait analysis. It istherefore difficult to continue this data collection for long
periods of time.
Another choice is to collect gait data by installing a camera to observe a well-trafficked street or somewhere similar
and by continuously recording the scene without any personnel on site. This approach, however, still suffers fromlaborious post-processing: the manual segmentation of individual subjects and annotation of the subject IDs for eachindividual. Moreover, because the captured subjects are
general people without any informed consent, the data capture itself may be problematic from the viewpoint of ethical, legal, and social issues (ELSI) (e.g., the protection ofprivacy and personal information).
To construct an extremely large-scale gait database while
taking the above points into account, we propose an automatic gait data collection system in conjunction with anexperience-based long-run exhibition of video-based gait
analysis1 . Note that this paper focuses not on the database
itself but on the system to collect gait data in large scale.
The contributions of this work are three-fold.
1. An online demonstration of state-of-the-art videobased gait analysis
A key to the success of collecting a large number of participants depends on an attractive demonstration. We therefore developed an online demonstration system of stateof-the-art video-based gait analysis composed of two main
components: (1) measurement of intuitive gait features suchas walking speed, stride, arm swing, and motion symmetry, and (2) gait-based age estimation. The participants are
1 “Just from walking”, the 15th media lab. at Miraikan (one of thelargest science musiumi in Japan), from 15th Jul. 2015 to 27th Jun. 2016,

given the opportunity to receive a printed card of their personal gait measurement results as well as to compare theirresults in a group such as with family and friends, which
can be an incentive for participation.
2. An automatic gait data collection system
In order that the participants can enjoy the demonstration by themselves and that the gait data can be collectedwithout any manual operation, we developed an automatic
system where a QR code is used to maintain subject ID andtrigger the demonstration, photo-electronic sensors are used
for passage detection to trigger various events, and speakers
for audio guidance are used so that the participant can easily
understand the next action they should take.
3. Data collection with informed consent
The system also provides a function to obtain informed
consent from the participants so that the collected data canbe used for research purposes, which was carefully implemented under supervision by a lawyer familiar with ELSI.
Therefore, the collected gait database can be made available
in the public domain to advance state-of-the-art video-based
gait analysis in the future.

2. Automatic gait data collection system
2.1. Overview
A system overview is given from a participant’s viewpoint along with Fig. 2. The participant first reads an explanation of the aim of the experience-based exhibition inparallel with gait data collection at the entrance panel. The
participant then goes into the demonstration site and visits a QR-code ticket machine (Fig. 3(a)), where more detailed explanation about the exhibition is provided. A QR
code that includes an assigned subject ID is issued to participants after they input gender and age information. They
subsequently enter the line for the online demonstration ofvideo-based gait analysis. They pass the QR code over the
QR-code reader (Fig. 3(b)) and then enter a walking coursein accordance with audio guidance, where a green chromakey background is installed.
The participant then walks to the other side, carrying any
items such as bags or backpacks, and then places these items
into a box. Subsequently, he or she walks two more rounds
and then picks up the items and leaves the walking course.
At the exit of the walking course, participants check the result of video-based gait analysis through a display and obtain a printed result if they press a button to agree to theresearch purpose use of their gait data (Fig. 3(c)). Furthermore, they can also compare the results with their family
members or friends through another display (Fig. 3(d)).

2.2. Demonstration unit
The demonstration unit comprises two processes: intuitive gait feature measurement and gait-based age estima-

: Photo-electronic sensor

3

Personal
items
box

Walking course: Camera for demonstration

2

4

Cam 7

1
QR-codereader

Display& printerCam 6: Camera for data collection

Display

Anotherdemonstration

Cam 5

Cam 4
Cam 3

Storage box
Exit

Cam 2

Cam 1

QR-code ticket
machineEntrance

Entrancepanel

Figure 2: System overview. A bilingual explanation is presented at each site.

Tablet PC(covered)

Receipt printer(covered)(b) (A simplified version of [20] was implemented.). The
ROI and thresholds for the preprocessing can be adjusted
through a graphical user interface (e.g., a scroll bar) while
confirming the results online, which enables us to easily
adapt the system to a particular demonstration site.

Display of
instruction

QR-codereader(a) QR-code ticketing machine

Display for resultwith informed
consent

Disagreebutton(b) QR-code reader

Display forcomparing
resultPrinter(covered)Agreebutton(c) Display and printerExplanatory notefor result(d) Display for comparing result

Figure 3: Key components of the exhibition.

tion. We implement the two processes using left-to-right
2.5-s walking image sequences (The start time for capture is determined by photo-electronic sensors, as describedlater.) of 640 × 480 pixels at 30 fps, captured by a USB
camera (PointGrey, FMVU-13S2C-CS). Because both processes rely on silhouette-based analysis, silhouette extraction is an important preprocessing step. For this purpose,the demonstration unit implements (1) region of interest(ROI) setting, (2) background subtraction with pixel-wise
single Gaussian background modeling, and (3) color-based
pixel-wise shadow removal, as shown in Figs. 4(a) andIntuitive gait feature measurement
In the following paragraphs, intuitive gait feature measurement is briefly described (see [17] for more technicaldetail). We consider eight intuitive gait features: walking speed, gait period, stride, step symmetry, forward arm
swing, backward arm swing, arm swing symmetry, andposture. We first apply morphological operation and maxregion filtering to obtain the silhouette sequence as well as
the bounding box sequence of the participant. Because thecamera used for this demonstration is calibrated in advanceand the ground plane constraint on the foot point is available, the walking speed is easily computed from the traveling distance between the start and end foot positions in theworld coordinates and elapsed time (i.e., 2.5 s).
We then compose a size-normalized silhouette sequence(Fig. 4(c)) and compute a gait period by maximizing
the normalized auto-correlation along with the temporalaxis [18]. Furthermore, we detect a series of single support
phases (SSPs) and double support phases (DSPs), wherearms and legs are the most closed and spread, respectively(Fig. 4(d)(e)). Subsequently, we compute individual strides
between adjacent DSPs based on the traveling distance andelapsed time. We then compute their average to determine
the stride and also compute the deviation as the left-rightfunction, i.e., an inner product between two feature vectors
x 𝑖 and x 𝑗 is defined as a radial basis function (RBF) kernel
so as to cope with the nonlinearity,()∣∣x 𝑖 − x 𝑗 ∣∣𝑘(x 𝑖 , x 𝑗 ; 𝑟) = exp −
,
(1)
2𝑟2
(a)

(b)

(c)

(d)

(e)

(f)

(g)

Figure 4: Intuitive gait feature measurement. From an original image and ROI (red rectangle) in (a), a silhouette (white)and shadow (gray) are extracted in (b). A size-normalized
silhouette sequence (every three frames) is shown in (c), anddetected SSPs and DSPs for all the steps are shown in (d)and (e), respectively. Areas swept by the arm swing aresuperimposed on an SSP frame (green: forward and red:
backward) in (f) and a central line segment from the head
to upper torso for posture measurement is depicted by a redline in (g).

step asymmetry.
We next extract front-side and back-side lines of thetorso from an SSP frame and compute the areas swept by
the arms in front of the front-side line and behind the backside line as forward and backward arm swings, respectively(Fig. 4(f)). Thereafter, the deviation of the areas swept byarms between pairs of adjacent SSPs is regarded as the letright arm swing asymmetry. Finally, we compute the central
line segment from the head to the upper torso; this inclination is used to measure the goodness of posture (Fig. 4(g)).
Gait-based age estimation
In the following paragraphs, gait-based age estimation isbriefly described and we refer the readers to [16] for moredetails about the baseline component.
We first extract the gait energy image (GEI) [5], i.e.,
the averaged silhouette [12], as a gait feature by averaging
the size-normalized silhouette sequence for one gait period.
Here, we unfold the GEI into a feature vector x ∈ ℝ𝑀 ,where 𝑀 is the dimension of the feature vector, i.e., theimage size of the GEI. We then adopt Gaussian process regression (GPR) [4] as an example-based approach to age
estimation.
For this purpose, let us assume that a training set 𝐷 =[𝑋, y ] is given, where 𝑋 = [x 1 , ⋅ ⋅ ⋅ , x 𝑁 ] is a set of 𝑁
samples of gait features and y = [𝑦1 , ⋅ ⋅ ⋅ , 𝑦𝑁 ] is a set ofthe corresponding ground truth ages. In addition, an affinity

where ∥ ⋅ ∥ is the 𝐿2 norm and 𝑟 is a hyper-parameter for
the RBF kernel.
Thereafter, given an input gait feature x ∗ , the GPR estimates the posterior probability distribution of age 𝑦∗ corresponding to gait feature x ∗ based on training set 𝐷. According to the theory of Gaussian processes, the posterior
probability distribution 𝑃 (𝑦∗ ∣x ∗ , 𝐷) is defined as a Gaussian distribution 𝒩 (𝑦∗ ; 𝜇𝑦 , 𝜎𝑦2 ), where mean 𝜇𝑦 and variance 𝜎𝑦2 are defined as
𝜇𝑦
𝜎𝑦2

=
=

k 𝑇∗ (𝐾 + 𝑆)−1 y𝑘(x ∗ , x ∗ ) − k 𝑇∗ (𝐾 + 𝑆)−1 k ∗ + 𝜎 2 ,

(2)
(3)

where 𝐾 is an 𝑁 × 𝑁 square matrix whose (𝑖, 𝑗)-th component is 𝑘(x 𝑖 , x 𝑗 ), k ∗ is an 𝑁 -dimensional vector whose𝑖-th row is 𝑘(x 𝑖 , x ∗ ), 𝑆 is an 𝑁 × 𝑁 diagonal matrix whose(𝑖, 𝑖)-th component is 𝜎 2 , and 𝜎 2 is observation noise variance for age, which is experimentally set as 𝜎 2 = 0.25.
In Eqs. (2) and (3), the most time consuming part is computing the inverse of an 𝑁 × 𝑁 matrix, i.e., (𝐾 + 𝑆)−1 ,which has a computational time complexity of 𝒪(𝑁 3 ) andmay make online computation difficult. We therefore employ a GPR with dynamic active set method [29], where thetraining set used is limited to the neighborhood of the input vector x ∗ . For example, if we choose 𝐾(≪ 𝑁 ) nearest
neighbors to the input vector x ∗ , the computational timecomplexity is 𝒪(𝐾 3 ) for the inverse matrix computation,
which is much less complex than 𝒪(𝑁 3 ), i.e., computationally tractable for an online demonstration.
On the other hand, a too-small 𝐾 may degrade the accuracy of the gait-based age estimation, and hence we need tochoose a suitable 𝐾 while considering the tradeoff between
accuracy and computational efficiency. We therefore conducted preliminary experiments to investigate the tradeoff
using a subset of the OU-ISIR Large Population dataset,
Camera 1, Version 2 (OULP-C1V2) [8], which contains
𝑁 = 1, 678 training subjects and 2,257 test subjects. Consequently, we determined the number of nearest neighbors
to be 𝐾 = 10. In addition, parameter 𝑟 for the RBF kernel is defined by taking the proximity of the chosen nearest
neighbors into account as𝐾

1 ∑∥x 𝑘𝑁 𝑁 𝐼𝐷(𝑘) − x ∗ ∥,
𝑟=
𝐾

(4)

𝑘=1

where 𝑘𝑁 𝑁 𝐼𝐷(𝑘) is a training sample index of the 𝑘-th
nearest neighbor to the input x ∗ .

Cam 1’Cam 2’
Cam 3’
Cam 4’
Cam 5’

Cam 7’Walking
direction

Cam 6’
Cam 7

Cam 6
Cam 5
: Camera foroutward part

Cam 4
Cam 3
Cam 2 Cam 1: Camera for
return part(a) Printed card for each participant.

Figure 6: Illustration of the camera setup.

(b) Comparison among six latest participants.

Figure 5: Display presented to the participants. In (a), themeasured intuitive gait features are shown by a radar chart
and inner, middle, and outer octagons indicate the minimum, average, and maximum values for each intuitive gaitfeature, respectively. In (b), the measured intuitive gait features are shown in a common radar chart using colors corresponding to individual participants.

Display to the participants
The results obtained by the intuitive gait feature measurement and gait-based age estimation are displayed to theparticipants in two ways. One is a printed card that contains measured intuitive gait features and estimated gaitbased age as well as a captured original image and a sizenormalized silhouette sequence for each participant (Fig.
5(a)). The other is a comparison of measured intuitive gaitfeatures and captured original videos of the six most recent
participants, where each participant is highlighted by a corresponding color (Fig. 5(b)).

2.3. Data collection unit
While the participant experiences the demonstration at
the walking course, gait data of 1,280 × 960 pixels at 25
fps are captured by seven network cameras (AXIS Communications, Q1614) placed at intervals of 15-deg azimuth angles along a quarter of a circle whose center coincides withthe center of the walking course. Its radius is approximately
8 m and height is approximately 5 m (Fig. 2). One exception is the camera for the front/back-view (Cam 7), whichis placed on the opposite side of the other cameras to avoid
occlusion by a brace for the green chroma-key structure.
As introduced in Section 2.1, each participant first walks
to the personal items box and puts his or her bags it into it,
and then walks two more rounds. Note that the observedview from the cameras to the participant for the return partis regarded simply as the opposite of that for the outward
part. More specifically, the participant is observed from theside-to-frontal view on the left side and also from the backfor the outward part (Cam 1 to Cam 7 in Figs. 2 and 6), andthen from the side-to-back view on the right side and also
from the front for the return part (Cam 1’ to Cam 7’ in Fig.
6).
As a result, a walking sequence with carrying status isobserved for the outward part (seven views) as well as two
normal walking sequences for each of the outward and return parts (14 views in total). We can therefore evaluate thegait recognition algorithms under a number of protocols, forexample, gait recognition under the same covariate condition, multi-view and cross-view gait recognition, and gait
recognition with and without carrying status. In addition,
we can also use the data for other video-based gait analyses,e.g., gait-based gender classification and age estimation.
Because each network camera continuously records thewalking course during the opening hours of the demonstration booth, we need to segment the video into pieces
containing individual subjects with corresponding subject
IDs. For this purpose, we exploit the log from the QRcode reader at the entrance to obtain the subject ID, andalso photo-electronic sensors to obtain the time the subject
passes them. More specifically, because we installed four
photo-electronic sensors at: (1) the entrance, (2) the startline of the outward part, (3) the start line of the return part,and (4) the exit, as shown in Fig. 2, we can pick up individual walking sequences for the outward and return parts,
which total five sub-sequences for each subject.

2.4. Informed consent
Because we want to use the collected gait data for our
research as well as to make it available in the public domain to facilitate video-based gait analysis in the researchcommunity, it is quite important to obtain informed consent for the research-purpose use of the collected gait datafrom the participants. For this purpose, we consulted with alawyer who is an authority on ELSI with respect to CCTVs
as to how to appropriately obtain the informed consent, andfurther obtained advice on a number of items ranging froma comprehensive viewpoint (i.e., the ground design of theexperience-based exhibition) to a specific viewpoint (i.e.,the specific text to be presented to the participants).
More specifically, announcements regarding the informed consent are repeatedly presented to the participant
at three sites. The first site is the entrance panel, which informs the participants of the aim of the experience-based
exhibition of video-based gait analysis in parallel to the gait
data collection. The second site is the QR-code ticketingmachine, where a similar explanation is presented to theparticipant on a tablet PC, and a QR code is issued only
if the participant agrees to it. Moreover, if the participant isa child, we request that a guardian read the explanation anddecide whether to agree on behalf of the child.
The third site is the display and printer at the exit of thewalking course. Here, we show a number of items: (1) the
result to be printed, (2) sample images captured by the seven
network cameras, and (3) an explanation of its research
purpose use and appropriate management based on privacy
laws. At this site, we installed physical buttons by which
participants declare agreement (a blue button) or disagreement (a red button) to this use of their data (Fig. 3(c)). If
the participant presses the agree button, the result is printed(Fig. 5(a)) and also appears in the display for comparison(Fig. 5(b)), otherwise, the result is not printed and does notappear on the display.

3. Results and discussion
3.1. Computational time
The online process is an important aspect of the demonstration, and hence we evaluated the computational times
for the two components: intuitive gait feature measurement
and gait-based age estimation. As a result, computationaltimes averaged over ten trials are 1.5 and 0.2 s, respectively. These two components begin processing just after
the participant completes the first return part. Thereafter,
the participant walks one and a half rounds more, whichclearly takes much more time than the total processing time

(1.7 s). Therefore, when the participant leaves the walking course, the result has been already displayed, i.e., theparticipant needs not wait for the result. Consequently, we
conclude that the computational time is acceptable for the
online demonstration.

3.2. Accuracy of gait-based age estimation
Another important aspect is the accuracy of the measured/estimated results. Because there is no ground truth
for the intuitive gait features, we evaluated the gait-based
age estimation by comparing it with the participant’s input
age at the QR-code ticketing machine.
More specifically, we evaluated the accuracy by themean absolute error (MAE) between the estimated ages
and the ground truth ages, with a subset of collected data
(5,298 subjects). As a result, we obtained 8.44 years MAE,
which is almost the same as that using the baseline algorithm (MAE: 8.4 years) reported in [16]. Since it takes morethan 30 s for gait-based age estimation if we use the fulltraining set without the active set method in the same way
as [16], which was confirmed by a preliminary experiment,
it turns out that our active set-based method realizes a goodtradeoff between the accuracy and computational cost for
gait-based age estimation.

3.3. Rate of agreement to the research purpose use
In order to know how our gait demonstration system attracts the participants, we investigate the rate of agreement
to the research purpose use of their gait data. As a result, 97.8% of the participants agreed to it and obtained theprinted results of the intuitive gait feature measurement andgait-based age estimation, which shows that our demonstration of video-based gait analysis can be a good incentive forparticipation.
This implies a possibility that other biometric researchers can also automatically collect large-scale biometric data with informed consent in a similar framework if
they provide an attractive biometric experience-based exhibition (e.g., facial age progression simulation or facial expression editing for face biometrics) in a right place such asa science museum.

3.4. Data collection progress
As of this publication, we have run the exhibition andcollected the gait data of 47,615 subjects for 246 days. Because the largest existing gait database in the world contains
4,016 subjects [8], the collected gait dataset has already exceeded it in size. Because we plan to continue the long-run
exhibition for approximately 11 months in total, we expectthat the number of subjects will ultimately exceed 70,000 in
number.
As a reference, subject statistics in terms of gender andage are shown in Fig. 7. We can see that a wide range

5,000

Male#Sbuejcts

4,000

Female

3,000
2,000

0

0-5
6-10
11-15
16-20
21-25
26-30
31-35
36-40
41-45
46-50
51-55
56-60
61-65
66-70
71-75
76-80
81-85
86-90
91-95
96-1001,000

Age range [year]

Figure 7: Subject statistics.

of ages and gender are covered except adults over 60 years
old. Because we are now planning to invite a group of older
adults (over 60 years old) to participate in the exhibition,
the biased age distribution is expected to be more or lessimproved.

3.5. Prospective use of collected data
Before discussing prospective use of the collected data,
we briefly review the existing gait databases. Important aspects for gait databases are (1) a large number of subjectsand (2) a variety of covariate conditions. Some publicly
available gait databases, such as the USF gait database [27],SOTON gait database [28], CASIA gait database [30], OUISIR Treadmill dataset [15], and TUM GAID [6], containseveral covariate conditions, such as views, clothing, andcarrying conditions. They do not, however, contain a sufficient number of subjects (i.e., at most 100 orders). The
only exception is the OU-ISIR large population dataset [8],which contains over 4,000 subjects. It does, however, notcontain a sufficient covariate conditions (i.e., relatively limited view variations from 55 deg to 85 deg). Moreover, wecannot say that the database with approximately 4,000 subjects is extremely large-scale.
On the other hand, we will collect over 70,000 subjects
by the end of the exhibition, with covariate conditions ofviews and carrying status, which meets the important aspects for the gait database.
Such an extremely large number of subjects are beneficial for video-based gait analysis. For instance, we canuse the collected data as training samples for example-based
approaches to gait-based age or gender estimation such as
GPR-based age estimation [16] used in this system.
Moreover, as for gait recognition task, it is well known
that standard deviation of false rejection rate (FRR) in averification scenario is approximately inverse proportion to
the squared root of 𝑁 − 1, where 𝑁 is the number ofsubjects in probes [21]. We can hence reduce the standard deviation of FRR approximately by one-fourth com-paredwith the OU-ISIR large population dataset [8] (i.e.,√
4000/70000 ≈ 1/4), which means 4-times statisticallyreliable performance of gait recognition is possible.
In addition, because the collected data contain the variations of view and carrying status with approximately
100-times larger number of subjects than the existing gait
database with covariate factors [6, 15, 27, 28, 30], we expect to improve the accuracy of machine learning-based approaches to robust gait recognition [10, 18, 23, 24] when thecollected data are used as training samples.

3.6. Limitations
While it is usually preferable to collect an enrollment(gallery) and a query (probe) of biometric data from different sessions within a certain time lapse, the collected gait
data contain few time lapses. This is an inevitable limitation of this kind of data collection framework because wecannot control the participants’ revisits in principle. To promote revisits, we need to seek more repeater-oriented contents for the exhibition (e.g., a regular health checkup byvideo-based gait analysis) as well as to issue a more robust
ID card for repeated use instead of a receipt with a QR codefor single-day use.
Moreover, development of the automatic data collection
system is more costly than manual data collection. However, this should be considered as a tradeoff between theinitial cost and running cost with respect to both budget andpersonnel. We believe that our choice, i.e., high initial cost
but low running cost, is suitable when collecting extremely
large-scale gait datasets.

4. Conclusion
This paper addresses an automatic gait data collection
system in conjunction with an experience-based exhibition.
We collected participants’ gait data with informed consent
while they enjoyed the online demonstration of state-of-theart video-based gait analyses: intuitive gait feature measurement and gait-based age estimation. Finally, as of this publication, we have actually run the experience-based exhibition with the cooperation of a science museum and collected
the gait data of 47,615 subjects.
After the long-running exhibition ends, we will construct
an extremely large-scale gait database and evaluate a number of standard video-based gait analysis algorithms. The
dataset will then be made available to the public to advancestate-of-the-art gait research.
Acknowledgment
This work was partly supported by JSPS Grants-in-Aid
for Scientific Research (A) 15H01693, Scientific Research(B) 16H02848, and the JST CREST ”Behavior Understanding based on Intention-Gait Model” project. We also appreciate Miraikan staff for this opportunity of data collection
in conjunction with a long-run exhibition.

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