Dashcam Video Curation for Generating
Memorial Movies on Tourism using Multiple
Measures of “Tourist Spot Likeness”⋆
Masaki Kawanaka1 , Yuki Matsuda1,2[0000−0002−3135−4915] , HirohikoSuwa1,2[0000−0002−8519−3352] , and Keiichi Yasumoto1,2[0000−0003−1579−3237]
1

Nara Institute of Science and Technology, Nara 630-0101, Japan{kawanaka.masaki.kj1, yukimat, h-suwa, yasumoto}@is.naist.jp
2
RIKEN Center for Advanced Intelligence Project (AIP), Tokyo 103-0027, Japan

Abstract. Recently, a method for the automatic generation of drivingsightseeing memorial route movies has been proposed by performing curation on videos captured by a dashcam. Existing methods curate videos
by scoring video frames with a single specific measure, however, it is hardto generate the appropriate memorial route movies due to the differencesin important perspectives for different users. To solve this problem, we
propose the method that represents “tourist spot likeness” with a combination of common measures derived by procedures used in the fieldof Affective Engineering and curates dashcam videos based on multiple
measures. In this paper, we surveyed these representative words to identify the measures that compose the measure of “tourist spot likeness”
and construct a model to estimate the score of each measure using dashcam video data as input. As the results of the experiment by using dashcam videos taken in Okinawa, we have derived six measures for curating
the dashcam videos and confirmed the proposed models, which estimate
each measure scored with a 7-level Likert scale achieved mean MAE of
0.64 (best: 0.52, worst: 0.73).
Keywords: Affective Engineering, Civic and Urban Computing, HumanCentered Artificial Intelligence, Location-based Services, Smart Tourism,
Video Curation, Dashcam

1

Introduction

In recent years, an increasing number of travelers have been posting memorial
videos created from photos and videos of their travels on social media. Most of
the memorial movie generation support systems, such as RealTimes [10], compile photos of multiple tourist spots. Demand for such systems that automatically create memorial videos is growing because they are easy to use, even forusers without video editing skills. However, few services provide memorial movie
generation support systems focusing on tourist routes, especially while driving.
⋆This study was supported in part by JST PRESTO under Grant No. JPMJPR2039.

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Masaki Kawanaka and Yuki Matsuda et al.

Since the trip between tourist spots constitutes a large percentage of sightseeing, many important scenes exist. It can be considered that the memorial tourist
movie generation support system focusing on tourist routes helps to share tourist
flow more intuitively than the system curates photos taken at multiple tourist
spots. In addition to public transportation such as buses and trains, walking
and driving are also considered as ways of tourist transportation. In sightseeing
using public transportation such as buses and trains, it is difficult to capture
tourist routes, but in the case of cars, it is possible to capture tourist routes
using a dashcam. As the performance of dashcams improves, they are not onlyused as evidence recorders in the event of accidents. Still, they are also utilized
in tourism support [9], driving skill improvement systems [12], and on-street
parking detection [7].
Katayama et al. [6] have developed a video curation algorithm to preserve
memorable scenes from a dashcam in a memorial video. The algorithm removes redundant parts, such as irreplaceable road trips, stops at traffic lights,
and traffic jams, and extracts memorable scenes that are the highlights of thevideo. To calculate the importance of the frame, they propose a measure named“Okinawa likeness”, which is the scale of how well the video frame matches theimpression of Okinawa (a prefecture in Japan). Then, the “Okinawa likeness”
of dashcam videos taken during sightseeing tours in Okinawa is collected andmeasured using crowdsourcing, and a machine learning model is constructedto predict “Okinawa likeness” for memorial video curation. However, ambiguous
expressions such as “Okinawa likeness” are perceived differently by different people [8], making it difficult to generate appropriate memorial route movies basedon the various “Okinawa likeness” assumed by each user, and also difficult toapply the model to other touristic areas.
This paper aims to improve the quality of curation of memorial route movies
by expressing “tourist spot likeness” using a combination of general-purpose
measures that are less prone to differences in interpretation among people, instead of measures that are prone to differences in interpretation used in previous
studies. To achieve this, it is necessary to clarify what combination of impressions people get from tourist spots can express “tourist spot likeness”. In
this paper, we use dashcam videos taken in Okinawa prefecture, one of Japan’s
most touristic southern islands. To derive a measure to be used for curating
the memorial route movie, we extract clusters of impression words that compose“tourist spot likeness” and select representative words based on procedures
used in the field of Affective Engineering. Then, we use these representative
words to identify measures of “tourist spot likeness”, and construct a CNN
model that estimates scores for each measure using dashcam videos. As a result,we identified six measures necessary for dashcam video curation, and confirmed
proposed models, which estimate each measure scored with a 7-level Likert scaleachieved mean MAE of 0.64 (best: 0.52, worst: 0.73).
This paper is organized as follows: In Section 2, we introduce related studies
and summarize the position of this research. Next, in Section 3, we describe amemorial video curation method using drive recorder images. In Section 4, weDashcam Video Curation using Measures of “Tourist Spot Likeness”

3

describe a method for identifying a measure of “tourist spot likeness” using
the results of extracting representative words that compose the measure, and in
Section 5, we construct and evaluate a CNN model for estimating the score ofeach measure. Finally, Section 6 concludes this paper.

2

Related Research

2.1

Video curation for route guidance

A system that provides route guidance must correctly communicate walking anddriving routes. Although maps are a simple route guidance system, some users
may have difficulty understanding a route with only a map. Therefore, methods
have been proposed to facilitate understanding of routes by utilizing curated
videos for route guidance [5]. In these methods, the playback speed is increased
for parts that are not necessary for route guidance, such as going straight, andcurated videos are played at normal speed for parts that are important for route
guidance, such as turning right or left. In a study for walking route guidance[5],the first-person video is used to detect landscape transitions based on histogram
differences, and curation is performed using a variable frame rate method. For
driving route guidance, we use video from a drive recorder to compute an opticalflow based on the LucasKanade algorithm to detect left-right turns and curate
videos.
2.2

Video curation for memorial video creation

In recent years, an increasing number of travelers have been posting memorial
videos created from photos and videos of their travels on social media. However,the creation of memorial videos using video editing software is difficult for users
who do not have video editing skills. For this reason, systems have been proposed
to automatically create memorial videos easily for people without video editing
skills [10]. However, memorial videos generated by conventional systems lackscenes during travel, a major element of tourism, and fail to reflect the impressive
scenes that occur during travel. Therefore, an automatic curation method formemorial videos of the entire tourist tour, including moving around, has been
proposed using dashcam video.
In the method of Katayama et al. [6], dashcam videos of tourist routes are segmented into 3-second segments, and the importance of each segment is estimatedto evaluate whether it is necessary as a tourist memorial video, and memorial
video curation is performed. As an importance estimation model, three image
frames are extracted from each 3-second segment video, and an estimation modelof “Okinawa likeness” was constructed using category occupancy calculated by
DeepLabv3 [2] trained with CityScapes [3] and BDD100k [14], and landmark
information obtained by YOLOv3 [11] as features. Crowdsourcing was used
to evaluate the “Okinawa likeness” of the segmented videos, which were thenused as the correct labels for the scores. However, since the vague expression

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Masaki Kawanaka and Yuki Matsuda et al.

“Okinawa likeness” is perceived differently by different people, it may not be
possible to generate memorial route movies based on the “Okinawa likeness”
assumed by each user.
2.3

Position of this research

In this study, we aim to improve the quality of memorial video curation by
expressing “tourist spot likeness” using a combination of measures thatare less likely to cause differences in interpretation among people, instead of ameasure that is more likely to cause differences in interpretation, based on thevideo curation method using the importance score proposed in Section 2.2. We
extract clusters of impression words that compose “tourist spot likeness”
and select representative words based on procedures used in the field of Affective
Engineering [13], to derive a measure for use in curating memorial route movies
using dashcam videos of tourist tours in Okinawa Prefecture.
In this paper, we use these representative words and factor analysis to identify
the measures that compose “tourist spot likeness”. Then, we construct a
CNN model to estimate the scores of each measure, and evaluate and discuss
whether the scores of each measure can be estimated or not.

3

Proposed System

The overview of the proposed method is shown in Fig. 1. First, the dashcam
video is split into 3-second segments, and the first, middle, and last frames are
extracted. When dividing the dashcam video into segmented videos, we divided
the video every 3 seconds, taking into account the characteristic that most people’s visual information enters a stable period in 3 seconds [1]. Three seconds
is the approximate time it takes for an object 50m ahead to frame out fromthe view of a car traveling at 60km per hour, which is enough time to confirm changes in the scenery outside the car. Next, the estimated scores of eachmeasure are obtained from machine learning models that estimate the measuresthat compose “tourist spot likeness”. Finally, segments are selected basedon the score of each measure, and a memorial tourist movie is generated.
The importance score used for selecting a segment is calculated from a combination of several measures, as shown in Fig. 2. In the proposed method, the“tourist spot likeness” is defined as follows, using the measures that compose “tourist spot likeness” and the weight parameter.
L

S=

1X
sl wl ,
L

(1)

l=1

where S denotes the “tourist spot likeness”, L denotes the total number of measures, w denotes the weight parameter of measure that composes“tourist spot likeness”. The proposed method can freely change the weightparameter w, and can generate a memorial route movie based on each user’s
idea of “tourist spot likeness”.

Dashcam Video Curation using Measures of “Tourist Spot Likeness”
ML model toestimate measure 1

Estimate scoreof measure 1

ML model toestimate measure 2

Estimate scoreof measure 2

5

Segment selection using
multiple measures

Input

Split into
segment video

Extract 3 frames fromsegmented video

・・・・・・・・・・・・

3sec
Dashcam video

OutputML model toestimate measure L

Memorialroute movie

Estimate scoreof measure L

Fig. 1. Overview of proposed method

Importance score such as“tourist spot-likeness”

Estimate scoreof measure 1

Estimate scoreof measure 2・・・・・・・・

Estimate scoreof measure

Fig. 2. Measures that compose “tourist spot likeness”

4

Selection of measures of “tourist spot likeness”

To realize the proposed method, it is necessary to clarify what kinds of combination of impressions that people receive from tourist spots can effectively
express “tourist spot likeness”. This section describes the method to derive “tourist spot likeness” measure.
Hereinafter, we describe the proposed method with an example of dashcam
videos taken in Okinawa prefecture, one of Japan’s most touristic southern islands.
4.1

Overview of measure derivation method

To derive the measures of “tourist spot likeness” to be used for the curation of memorial route movies, we employ the following procedure inspired by themethod in the field of Affective Engineering: (1) Collecting the impression words
when watching the dashcam video, (2) Conduct a psychological experiment tocalculate the distance in semantic space between impression words, extract clusters of impression words expressing “tourist spot likeness” by hierarchicalcluster analysis based on the obtained distance, and select representative words,(3) Using the representative words obtained and factor analysis, a measure of“tourist spot likeness” is derived.

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Masaki Kawanaka and Yuki Matsuda et al.

Table 1. Representative words that constitute “tourist spot likeness” for Okinawa
prefecture in Japan
Cluster 1

Cluster 2

Cluster 2

Cluster 4

Cluster 5radiant

comfortablerelax

seasidesuburbanpeaceful（晴れやかな）（気持ちいい）（のんびりした）（海沿いの）

（郊外の）

（平穏な）

loosely

coastalbrightlypleasant（明るい）（心地良い）（ゆったりとした） （海岸沿いの）

Cluster 6

quiet

composed（閑静な）（落ち着いた）

fresh

calm

refinedroutinesilent（清々しい）（穏やかな）

（優雅な）

（日常の）

（静かな）

Cluster 7

Cluster 8

Cluster 9

Cluster 10

Cluster 11

Cluster 12

spacious

warm

dazzling

expressway

exoticlively（開放的な）

（暖かい）

（眩しい）

（高速道路の） （異国情緒な） （賑やかな）

open

animatedsummernight

foreign

hot

（開けた）

（生き生きした）

（常夏の）

（夜の）

（異国の）

（暑い）

roadside

Asian

vast

tropical

new

（広大な）

（南国な）

（新しい）

（道路沿いの） （アジア風の）Cluster 13
messy

Cluster 14
trafficy

Cluster 15
urban

Cluster 16
dark（ごみごみした）

（渋滞の）

（都会の）

（暗い）

congestion

crowdeddowntown

narrow（渋滞した）

（雑踏の）

（街中の）

（狭い）

stuffycrampeddarkness（息苦しい）（窮屈な）（暗がりの）First, we have conducted procedures (1) and (2) using dashcam videos of Okinawa, and the representative words of each cluster obtained by cluster extraction
are shown in Table 1.
4.2

Derivation of measures of “tourist spot likeness”

In order to derive a measure of “tourist spot likeness”, we used Yahoo!
Crowdsourcing3 to conduct a task in which respondents were asked to rate, on
a 7-point scale (1. Strongly agree, 2. agree, 3. Slightly agree, 4. Neither agree
nor disagree, 5. Slightly disagree, 6. disagree, 7. Strongly disagree), how they felt
about the words shown in Table 1 when watching a dashcam video of Okinawa.
The videos used in this experiment were fifty 3-second segment videos generated
from dashcam videos taken in Okinawa. In the segment videos, we used dashcam
videos of various scenes, as shown in Fig. 3, so that various impression words
could be collected. For each video, 10 participants rated how they felt about thethree representative words that were presented when they watched the video. If
a person did not correctly answer a check question, all of his or her data werediscarded.
3

https://crowdsourcing.yahoo.co.jp/

Dashcam Video Curation using Measures of “Tourist Spot Likeness”

7Fig. 3. Segment video created from drive recorder video taken in Okinawa

A factor analysis using the maximum likelihood method and Promax Rotation was then performed on the means of each evaluation score for each of the 10videos obtained by crowdsourcing. The number of factors was determined to be
7, based on the criterion that the eigenvalue must be greater than 1. However,as the number of measures increases, the usability of the curation system may
decrease because users are required to select which measure is more importantwhen using the system. Therefore, the number of factors in this experiment was
set to 6.
The top three absolute values of factor loadings (FL) for representative
words showing positive and negative correlations for each factor obtained byfactor analysis are shown in Table 2. Here, we define and name the measure of“tourist spot likeness” for each factor. Factor 1 is positively correlated withwords that indicate less crowdedness, such as “quiet” and “silent”, and negatively
correlated with words that indicate more crowdedness, such as “congestion” and“messy”, thus Factor 1 is named as “urbanness”. Factor 2 has a positive correlation with words that describe the morning and afternoon hours, such as“brightly” and “radiant”, and a negative correlation with words that describe
the evening hours, such as “darkness” and “night”, thus Factor 2 is named as“brightness”. Factor 3 is named as “exoticness” because of its strong correlation with words that indicate exoticism, such as “exotic” and “tropical”, and Factor 4 is named as “scenicness” because of its strong correlation with words thatindicate scenery, such as “seaside” and “coastal”. Factor 5 is positively correlatedwith words that indicate more exhilaration, such as “expressway”, and negatively
correlated with words that indicate less exhilaration, such as “downtown” and“relax”, thus Factor 5 is named as “liveliness”. Factor 6 has a positive correlation with words that describe the open view, such as “open” and “vast”,
and a negative correlation with words that describe the less open view, such as“narrow” and “cramped”, thus Factor 6 is named as “openness”. The results ofthe factor analysis indicate that the six measures of “tourist spot likeness”
are “urbanness”, “brightness”, “exoticness”, “scenicness”, “liveliness”,
and “openness”.

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Masaki Kawanaka and Yuki Matsuda et al.

Table 2. Top three absolute magnitudes of factor loadings (FL) for representative
words showing positive and negative correlations for each factor, and measure names
Factor 1
quiet
（閑静な）

positive correlation

silent（静かな）relax（のんびりした）

congestion（渋滞した）

negative correlation

trafficy
（渋滞の）

messy
（ごみごみした）FL

Factor 2

0.98

（明るい）

0.91

（晴れやかな）

0.87

（温かい）

-0.99

（暗がりの）

-0.91

（暗い）

-0.88

（夜の）

⇓
“urbanness”
Factor 4
seaside（海沿いの）

positive correlation

coastal（海岸沿いの）

vast
（広大な）

routine（日常の）negative correlation

lively（賑やかな）

narrow（狭い）FLradiant
warm
darkness
dark
night

（異国情緒な）

0.82

（異国の）

0.51

（南国な）

-0.97

（高速道路の）

-0.74
-0.72

exoticforeigntropical
expressway
suburban（郊外の）congestion（渋滞した）

0.91
0.88
0.974
-0.43
-0.27
-0.14

Factor 5

Factor 6

expressway

0.91

（新しい）

0.35

（眩しい）

-0.35

（日常の）

-0.16

0.87

FL

⇓
“exoticness”（高速道路の）-0.24

Factor 3⇓“brightness”

0.92⇓“scenicness”

5brightlyFL

newdazzlingroutinedowntown
（街中の）

relax
（のんびりした）

FL

open

0.86

（開けた）

0.50

（道路沿いの）

0.37

（広大な）

-0.42
-0.36
-0.33

⇓
“liveliness”

roadsidevastnarrow（狭い）

cramped（窮屈な）Asia-like（アジア風の）

FL
0.47
0.43
0.42
-0.81
-0.29
-0.26

⇓
“openness”

Building of “tourist spot likeness” estimation model

In this section, we build and evaluate a model to estimate scores using dashcam
video data as input for the six measures of “tourist spot likeness” derived
in Section 4.
5.1

DatasetIn this experiment, we use dashcam videos taken over four days from July 11
to 14, 2020 in Okinawa. When the dashcam video was divided into 3-second
segmented videos, 459 videos were generated on the first day, 831 videos on thesecond day, 1,244 videos on the third day, and 569 videos on the fourth day.
In total, 3,103 videos are included in the dataset. We collect the ground-truth

Dashcam Video Curation using Measures of “Tourist Spot Likeness”
Urbanness

Brightness

Count of data

400

Day 1
Day 2
Day 3
Day 4

300

Day 1
Day 2
Day 3
Day 4

300

400

200

100

100

100

1

2

3

4

5

6

7

0

1

2

3

4

5

6

7

0

Day 1
Day 2
Day 3
Day 4

300

400

Day 1
Day 2
Day 3
Day 4

300

100

100

100

4

True label

5

6

7

0

1

2

3

4

True label

4

5

6

7

5

6

7

Day 1
Day 2
Day 3
Day 4

300
200

3

3

400

200

2

2

Openness

200

1

1

Liveliness

400

0

Day 1
Day 2
Day 3
Day 4

300

200

Scenicness

Count of data

Exoticness

400

200

0

9

0

1

2

3

4

True label

5

6

7

Fig. 4. Relationship between the number of true labels and the number of data in eachsessionlabels of how people perceive the measure of “tourist spot likeness” when
watching the videos, by using Yahoo! Crowdsourcing. Each video is annotated by
five people, and the ground-truth label is derived by averaging them. If a person
did not correctly answer a dummy question for detecting careless responses, allof their data were discarded and were not included in the following analysis. To
inform the meaning of each measure, we provided the following explanations to
crowd workers in advance. The “urbanness” measure is closer to 1, the more
urban it is, and the closer it is to 7, the more rural it is. The “brightness”
measure is closer to 1 for brighter, and closer to 7 for darker. The “exoticness”
measure is closer to 1, indicating a sense of exoticism, and closer to 7, indicating a lack of a sense of exoticism. The “scenicness” measure is closer to 1,
indicating beautiful scenery, and closer to 7, indicating less beautiful scenery.
The “liveliness” measure is closer to 1, indicating exhilaration, and closer to
7, indicating convergence. The “openness” measure is closer to 1, indicating a
sense of openness, and closer to 7, indicating a sense of tightness.
5.2

Experimental conditions

In this experiment, we use ResNet [4], which has been shown to be effective in thefield of image recognition, to estimate the scores of the measures that constitute“tourist spot likeness”. The model is a trained model of ResNet50 provided
by PyTorch. As shown in Fig. 1, the dashcam video is divided into 3-second segment videos, and three images extracted from the 3-segment videos are used as
input. The three images used as input are the first, middle, and last frames of the
3-second segment video. When inputting the images into the ResNet, the three
images were concatenated in the dimensionality direction where RGB is stored.
As output, the number of output classes was changed to 1 to obtain a score

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Masaki Kawanaka and Yuki Matsuda et al.

Table 3. Results of the evaluation of six measure score estimation models（MAE）
urbanness brightness exoticness scenicness liveliness openness Mean
0.73

0.52
Urbanness

True score

7

0.65

0.59

Brightness

7
6

6

5

5

5

4

4

4

3

3

3

2

2

2

1

2

3

4

5

6

7

Scenicness

7

1

1

2

3

4

5

6

7

Liveliness

7

1

6

6

5

5

5

4

4

4

3

3

3

2

2

2

1

2

3

4

5

Estimate score

6

7

1

1

2

3

4

5

Estimate score

6

7

1

0.64

Exoticness

1

2

3

4

5

6

7

5

6

7

Openness

7

6

1

0.60

7

6

1

True score

0.72

1

2

3

4

Estimate score

Fig. 5. Relationship between the true values and the estimated results of the sixmeasure score.

for each measure and treated as a regression model. To evaluate generalization
performance, leave-one-day-out cross-validation was conducted with one day’s
worth of data as the unit. The relationship between the number of true labels
and the number of data in each session is shown in Fig. 4. Mean Absolute Error(MAE) was used as the loss function during training, and Stochastic Gradient
Descent (SGD) was used as the optimization method with a learning rate of
0.01. The number of mini-batches was 32 and the number of epochs was 20.
5.3

Experimental results

The results of the MAE evaluation are shown in Table 3, and the results of theestimated measure scores using the ResNet are shown in Fig. 5. Table 3 showsthat the mean of MAE for all measures is about 0.64, which indicates that the results are relatively good. Furthermore, Fig. 5 shows that the estimated scores forDashcam Video Curation using Measures of “Tourist Spot Likeness”

11“brightness”, “scenicness”, “liveliness”, and “openness” increased as thescore of the true label increased, indicating that the results captured the trend.
On the other hand, for “urbanness” and “exoticness”, the estimated scores
were concentrated around the center (3–5), indicating that the estimates were
not correct. One possible cause of this result is the influence of data bias causedby the division of the test data when conducting the leave-one-day-out crossvalidation. As shown in Fig. 4, the data around the true label 6 for “urbanness”
are more abundant in Day 3 than in the other data, and the model using Day 1,
2, and 4 as training data is not able to correctly learn the data around the truelabel 6, and thus is considered to be unable to estimate it.

6Conclusion

The purpose of this study is to improve the quality of memorial video curation by
expressing “tourist spot likeness” using a combination of general-purpose
measures that are less likely to be interpreted differently by different people
instead of the measures that have been used in existing studies and that areprone to differences in interpretation. In this paper, we derived six measuresof “tourist spot likeness” and constructed a CNN model to estimate eachmeasure of dashcam data taken in Okinawa prefecture. The experimental results
show that the mean MAE of all the measures is about 0.64, which is relatively
good. On the other hand, due to data imbalance problems, it became clearthat we could not construct a model that shows sufficient performance for somemeasures.

References
1. Brady, T.F., Konkle, T., Alvarez, G.A., Oliva, A.: Visual long-term memory has a
massive storage capacity for object details. Proceedings of the National Academy of
Sciences 105(38), 14325–14329 (2008). https://doi.org/10.1073/pnas.0803390105
2. Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv preprint arXiv:1706.05587 pp. 1–14
(2017). https://doi.org/10.48550/arXiv.1706.05587
3. Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson,
R., Franke, U., Roth, S., Schiele, B.: The Cityscapes Dataset for Semantic Urban Scene Understanding. In: Proc. of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3213–3223. CVPR ’16 (2016).
https://doi.org/10.1109/CVPR.2016.350
4. He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition.
In: 2016 IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–
778. CVPR ’16 (2016). https://doi.org/10.1109/CVPR.2016.90
5. Kanaya, Y., Kawanaka, S., Suwa, H., Arakawa, Y., Yasumoto, K.: Automatic Route Video Summarization based on Image Analysis for Intuitive Touristic Experience. Sensors and Materials 32(2), 599–610 (2020).
https://doi.org/10.18494/SAM.2020.2616

12

Masaki Kawanaka and Yuki Matsuda et al.

6. Katayama, Y., Suwa, H., Yasumoto, K.: dash-cum: Dashcam Video Curation for
Memorial Movie Generation. In: The 27th Symposium on Information Systems forSociety. ISS ’21 (2021), (in Japanese)
7. Matsuda, A., Matsui, T., Matsuda, Y., Suwa, H., Yasumoto, K.: A Method for
Detecting Street Parking Using Dashboard Camera Videos. Sensors and Materials
33(1), 17–34 (2021). https://doi.org/10.18494/SAM.2021.2998
8. Matsuda, Y.: IoPT: A Concept of Internet of Perception-aware Things. In: The
12th International Conference on the Internet of Things. pp. 201–204. IoT ’22(2022). https://doi.org/10.1145/3567445.3571108
9. Morishita, S., Maenaka, S., Nagata, D., Tamai, M., Yasumoto, K., Fukukura, T.,
Sato, K.: SakuraSensor: Quasi-Realtime Cherry-Lined Roads Detection throughParticipatory Video Sensing by Cars. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. pp. 695–705.
UbiComp ’15 (2015). https://doi.org/10.1145/2750858.2804273
10. RealNetworks: RealTimes. https://jp.real.com/realtimes/, (Accessed on Dec. 28,
2022)
11. Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement. arXiv preprint
arXiv:1804.02767 pp. 1–6 (2018). https://doi.org/10.48550/arXiv.1804.02767
12. Takenaka, K., Bando, T., Nagasaka, S., Taniguchi, T.: Drive Video Summarization
Based on Double Articulation Structure of Driving Behavior. In: Proceedings ofthe 20th ACM International Conference on Multimedia. pp. 1169–1172. MM ’12(2012). https://doi.org/10.1145/2393347.2396410
13. Tobitani, K., Matsumoto, T., Tani, Y., Nagata, N.: Modeling the Relation between Skin Attractiveness and Physical Characteristics. In: Proceedings of the2018 International Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia. pp. 30–35. MMArt&ACM ’18 (2018).
https://doi.org/10.1145/3209693.3209699
14. Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., Darrell,T.: BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning.
In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp.
2633–2642. CVPR ’20 (2020)