Faculty Profiles - Deguchi Daisuke

Presentations -

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A study on wheelchair-users detection in a crowded scene by integrating multiple frames

Ukyo Tanikawa, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Tatsuo Akiyama

IEICE Technical Report (PRMU) 2018.1.18

Efficient Pedestrian Scanning by Active Scan LIDAR International conference

Taiki Yamamoto, Fumito Shinmura, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase

International Workshop on Advanced Image Technology (IWAIT) 2018 2018.1.8

Pedestrian Detection from Sparse Point-Cloud using 3DCNN International conference

Yoshiki Tatebe, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase, Utsushi Sakai

International Workshop on Advanced Image Technology (IWAIT) 2018 2018.1.8

地域・時期ごとのSNS投稿写真に基づく類似地域マイニング

陳璐, 川西康友, 井手一郎, 平山高嗣, 道満恵介, 出口大輔, 村瀬洋

画像の認識・理解シンポジウム(MIRU) 2018.8.7

Event date： 2018

Language：Japanese Presentation type：Poster presentation

Venue：札幌コンベンションセンター

知らない土地に旅行する際，その土地での旅行計画を円滑に進めるためには，行き先の地域別の雰囲気を理解することが重要である．ある地域が，既に知っている地域の雰囲気と似ていることが分かれば，その地域の雰囲気を直感的に把握できる．従来研究は，地域ごとのSNS 投稿写真を用いて地域間の類似性を計算することにより，雰囲気が似ている場所をマイニングしている．我々はこの研究を発展させ，各写真の撮影時期を考慮し，時期毎の類似地域をマイニングする手法を提案する．

Voting-based Hand-Waving Gesture Spotting from a Low-Resolution Far-Infrared Image Sequence International conference

Yasutomo Kawanishi, Chisato Toriyama, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Tomoyoshi Aizawa, Masato Kawade

2018 IEEE International Conference on Visual Communications and Image Processing (VCIP2018) 2018.12.11

Event date： 2018

Language：English Presentation type：Oral presentation (general)

Venue：永豐棧酒店臺中（臺中，臺灣）

We propose a temporal spotting method of a hand gesture from a low-resolution far-infrared image sequence captured by a far-infrared sensor array. The sensor array captures the spatial distribution of far-infrared intensity as a thermal image by detecting far-infrared waves emitted from heat sources. It is difficult to spot a hand gesture from a sequence of thermal images captured by the sensor due to its low-resolution, heavy noise, and varying duration of the gesture. Therefore, we introduce a voting-based approach to spot the gesture with template matching-based gesture recognition. We confirm the effectiveness of the proposed temporal spotting method in several settings.

DOI： 10.1109/VCIP.2018.8698650

Gaze-inspired Learning for Estimating the Attractiveness of a Food Photo International conference

Akinori Sato, Takatsugu Hirayama, Keisuke Doman, Yasutomo Kawanishi, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

20th IEEE Int. Symposium on Multimedia (ISM2018) 2018.12.10

Event date： 2018

Language：English Presentation type：Oral presentation (general)

Venue：臺中金典酒店（臺中，臺灣）

The number of food photos posted to the Web has been increasing. Most of the users prefer to post delicious-looking food photos. They, however, do not always look delicious. A previous work proposed a method for estimating the attractiveness of food photos, that is, the degree of how much a food photo looks delicious, as an assistive technology for taking a delicious-looking food photo. This method extracted image features from the entire food photo to evaluate the impression. In our work, we conduct a preference experiment where subjects are asked to compare a pair of food photos and measure their gaze. The proposed method extracts image features from local regions selected based on the gaze information and estimates the attractiveness of a food photo by learning regression parameters. Experimental results showed the effectiveness of extracting image features from outside the gaze regions rather than inside them.

DOI： 10.1109/ISM.2018.00015

Localizing the Gaze Target of a Crowd of People International conference

Yuki Kodama, Yasutomo Kawanishi, Takatsugu Hirayama, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Hidehisa Nagano, Kunio Kashino

International Workshop on Attention/Intention Understanding 2018.12.3

Event date： 2018

Language：English Presentation type：Oral presentation (general)

Venue：Perth Convention and Exhibition Centre 21 Mounts Bay Rd Perth WA 6000 Australia

What target is focused on by many people?
Analysis of the target is a crucial task, especially in a cinema, a stadium, and so on.
However, it is very difficult to estimate the gaze of each person in a crowd accurately and simultaneously with existing image-based eye tracking methods, since the image resolution of each person becomes low when we capture the whole crowd with a distant camera.
Therefore, we introduce a new approach for localizing the gaze target focused on by a crowd of people.
The proposed framework aggregates the individually estimated results of each person's gaze.
It enables us to localize the target being focused on by them even though each person's gaze localization from a low-resolution image is inaccurate.
We analyze the effects of an aggregation method on the localization accuracy using images capturing a crowd of people in a tennis stadium under the assumption that all of the people are focusing on the same target, and also investigate the effect of the number of people involved in the aggregation on the localization accuracy.
As a result, the proposed method showed the ability to improve the localization accuracy as it is applied to a larger crowd of people.

Campus-wide Implementation of NU Kamirepo System for Handling Handwritten Assignments with an LMS

Daisuke Deguchi, Shunya Seiya, Shigeki Ohira, Tomoki Toda

2018.11.19

Analyzing Headlight Flicker Patterns for Improving the Pedestrian Detectability from a Driver International conference

Takashi Maeda, Takatsugu Hirayama, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

2018 IEEE 21st International Conference on Intelligent Transportation Systems (ITSC2018) 2018.11.7

Event date： 2018

Language：English Presentation type：Oral presentation (general)

Venue：Maui, Hawaii, USA

In this paper, we analyze headlight flicker patterns which improve the pedestrian detectability from a driver. Recently, headlights are becoming capable of selectively projecting light on a pedestrian in addition to the normal forward projection. However, it is still not clear how the light should be
projected to effectively improve the visibility of the pedestrian. We actually analyze nine flicker patterns by controlling duty ratios and durations of lighting time, and conduct experiments in field and laboratory settings. As a result, we reveal that a specific fundamental frequency is effective for improving the pedestrian detectability from a driver. We also conclude that the difference between the two settings are not significant.

Attribute-aware Semantic Segmentation of Road Scenes for Understanding Pedestrian Orientations International conference

Mahmud Dwi Sulistiyo, Yasutomo Kawanishi, Daisuke Deguchi, Takatsugu Hirayama, Ichiro Ide, Jiang-Yu Zheng, Hiroshi Murase

2018 IEEE 21th International Conference on Intelligent Transportation Systems (ITSC2018) 2018.11.7

Event date： 2018

Language：English Presentation type：Oral presentation (general)

Venue：Maui, Hawaii, USA

Semantic segmentation is an interesting task for many deep learning researchers for scene understanding. However, recognizing details about objects’ attributes can be more informative and also helpful for a better scene understanding in intelligent vehicle use cases. This paper introduces a method for simultaneous semantic segmentation and pedestrian attributes recognition. A modified dataset built on top of the Cityscapes dataset is created by adding attribute classes corresponding to pedestrian orientation attributes. The proposed method extends the SegNet model and is trained by using both the original and the attribute-enriched datasets. Based on an experiment, the proposed attribute-aware semantic segmentation approach shows the ability to slightly improve the performance on the Cityscapes dataset, which is capable of expanding its classes in this case through additional data training.

Estimating the Scene-wise Reliability of LiDAR Pedestrian Detectors International conference

Haruya Kyutoku, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Kazuki Kato, Hiroshi Murase

2018 IEEE 21th International Conference on Intelligent Transportation Systems (ITSC2018) 2018.11.7

Event date： 2018

Language：English Presentation type：Oral presentation (general)

Venue：Maui, Hawaii, USA

Nowadays, development of driving support systems and autonomous driving systems have become active.
Pedestrian detection from in-vehicle sensors is one of the most important technologies for these systems.
However, outputs of pedestrian detectors can not be fully trusted in real environments.
Therefore, we propose an estimation system of pedestrian detector's reliabilities for a given scene.
This paper proposes a scene-wise reliability calculation method for LiDAR-based detectors, and a construction method for their estimators.
Here, the problem is how we can define the reliability.
The proposed method defines the reliability considering oversights as the strictest threshold without oversights.
Meanwhile, it defines the reliability considering false detections as the loosest threshold without false detections.
Experimental results showed that the proposed method could properly represent the reliability of a given scene, and estimate their reliability.

Estimation of Driver's Insight for Safe Passing based on Pedestrian Attributes International conference

Fumito Shinmura, Yasutomo Kawanishi, Daisuke Deguchi, Takatsugu Hirayama, Ichiro Ide, Hiroshi Murase, Hironobu Fujiyoshi

2018 IEEE 21th International Conference on Intelligent Transportation Systems (ITSC2018) 2018.11.6

Event date： 2018

Language：English Presentation type：Oral presentation (general)

Venue：Maui, Hawaii, USA

In order to reduce traffic accidents between a vehicle and a pedestrian, recognition of a pedestrian who has a possibility of collision with a vehicle should be helpful. However, since a pedestrian may suddenly change his/her direction and cross the road, it is difficult to predict his/her behavior directly. Here, we focus on the fact that experienced drivers usually pass by a pedestrian while preparing to step on the brake at any moment when they feel danger. If driver assistant systems can estimate such experienced driver’s decisions, they could early detect the pedestrian in danger of collision. Therefore, we classify the driver’s decisions into three types by referring to the accelerator operation of drivers, and propose a method to estimate the type of the driver’s decision. The drivers are considered to decide their actions focusing on various behaviors and states of a pedestrian, namely pedestrian’s attributes. Since the driver’s decisions change along the timeline, the use of a temporal context is considered to be effective. Thus, in this paper, we propose an estimation method using a recurrent neural network architecture with the pedestrian’s attributes as input. We constructed a dataset collected by experienced drivers in control of the vehicle and evaluated the performance, and then confirmed the effectiveness of the use of pedestrian’s attributes.

自動運転車両の実現に向けた車載カメラによる環境理解

出口大輔

日本光学会年次学術講演会 2018.11.2

A Study on Similar Geo-region Mining based on Spatio-temporal Clustering of Social Media Photos

Chen Lu, Yasutomo Kawanishi, Ichiro Ide, Takatsugu Hirayama, Keisuke Doman, Daisuke Deguchi, Hiroshi Murase

IEICE Technical Report (MVE) 2018.10.26

Estimation of Driver's Behavior based on Pedestrian's Attributes when Passing by a Pedestrian

Fumito Shinmura, Yasutomo Kawanishi, Daisuke Deguchi, Takatsugu Hirayama, Ichiro Ide, Hiroshi Murase, Hironobu Fujiyoshi

2018.10.17

Scene-wise Reliability Estimation of Pedestrian Detection using LiDAR

Haruya Kyutoku, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Kazuki Kato, Hiroshi Murase

2018.10.17

Epipolar geometry-based ego-localization using an in-vehicle monocular camera International conference

Haruya Kyutoku, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Workshop on Digital Signal Processing for In-Vehicle Systems 2018.10.8

Event date： 2018

Language：English Presentation type：Poster presentation

Venue：Nagoya University, Japan

Nowadays, development of driving support systems and autonomous driving systems have become active.
Vehicle ego-localization using in-vehicle sensors is one of the most important technologies for these systems.
Accordingly, various attempts to localize own vehicle from in-vehicle sensors have been made.
In general, the estimation accuracy of the traveling direction is lower than in the lateral direction.
Therefore, we propose a highly accurate method for ego-localization of the traveling direction based on epipolar geometry using an in-vehicle monocular camera.
The proposed method makes correspondences between in-vehicle camera images and database images with location information, and calculates the location using locations annotated to the corresponding database images.
However, there are many gaps due to the difference of speed and trajectory of vehicles even if the images are obtained along the same road.
To overcome this problem, the distance between the input image and the database image is calculated by the distance metric based on the epipolar geometry and the local feature method.
An experiment was conducted using actual images with correct locations, and we confirmed the effectiveness of the proposed method from its results.

Vehicle counting via car parts detection from an in-vehicle camera image International conference

Fumito Shinmura, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Workshop on Digital Signal Processing for In-Vehicle Systems 2018.10.8

Event date： 2018

Language：English Presentation type：Poster presentation

Venue：Nagoya University, Japan

This paper proposes a method to count vehicles from an in-vehicle camera image by regression based on car parts detection. In the case of an in-vehicle camera image, since vehicles are frequently occluded by other vehicles in traffic congestion, it is difficult to accurately count vehicles. Therefore, we propose a method to count vehicles by regression based on the number of visible car parts. For this, we make an estimator by learning the relation between the number of visible car parts and that of vehicles by Support Vector Regression. We evaluated our method using in-vehicle camera images recorded in an actual environment, where the proposed method performed better than counting detected vehicles.

HOYO: A Gait Dataset Annotated with Mimetic Words

Hirotaka Kato, Takatsugu Hirayama, Keisuke Doman, Yasutomo Kawanishi, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

IEICE Technical Report (MVE) 2018.9.7

地域・時期ごとのSNS投稿写真に基づく類似地域マイニング

陳璐, 川西康友, 井手一郎, 平山高嗣, 道満恵介, 出口大輔, 村瀬洋

電子情報通信学会魅力工学研究会シンポジウム 2018 2018.9.5

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