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論文 - 出口　大輔

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Median-Shape Representation Learning for Category-Level Object Pose Estimation in Cluttered Environments 査読有り

Hiroki Tatemichi, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Ayako Amma, Hiroshi Murase

Proceedings of the 25th International Conference on Pattern Recognition 頁： 4473 - 4480 2021年1月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

In this paper, we propose an occlusion-robust pose
estimation method of an unknown object instance in an
object category from a depth image. In a cluttered
environment, objects are often occluded mutually. For
estimating the pose of an object in such a situation, a
method that de-occludes the unobservable area of the object
would be effective. However, there are two difficulties;
occlusion causes offset between the actual object center
and the center of its observable area, and different
instances in a category may have different shapes. To cope
with these difficulties, we propose a two-stage
Encoder-Decoder model to extract features with objects
whose centers are aligned to the image center. In the
model, we also propose the Median-shape Reconstructor as
the second stage to absorb shape variations in a category.
By evaluating the method with both a large-scale virtual
dataset and a real dataset, we confirmed the proposed
method achieves good performance on pose estimation of an
occluded object from a depth image.

超低解像度FIR画像系列中での人物位置と行動の違いに着目した骨格推定法査読有り

岩田紗希, 川西康友, 出口大輔, 井手一郎, 村瀬洋, 相澤知禎

精密工学会誌 87 巻 ( 1 ) 頁： 99 - 106 2021年1月

記述言語：日本語掲載種別：研究論文（学術雑誌）

Elderly monitoring systems are gaining attention in the modern aging society. For the purpose, Far-InfraRed (FIR) sensors are often used, because they can avoid privacy concerns and are robust to environmental lightings. The authors have previously proposed several methods for human skeleton estimation from an extremely low-resolution FIR image sequence whose resolution is 16 × 16 pixels. For more accurate estimation, this paper proposes a method that is robust to variations of human positions and actions in the FIR sequences. Specifically, to extract features robust to the human positions from the images by using a Convolutional Neural Network (CNN), a global max-pooling layer is inserted into the last layer instead of multiple pooling layers which are not suitable for low-resolution inputs. Also, a network with two branches is introduced that focuses on capturing spatial and temporal information respectively. Moreover, the network has a weighted sum mechanism of their outputs, which depends on the human actions. For evaluation, a dataset was created by capturing action sequences of a human at various positions in the FIR images. Through an experiment, we confirmed that the human motion can be smoothly estimated and that the estimation accuracy is improved by the proposed method.

Imageability Estimation using Visual and Language Features 査読有り

Chihaya Matsuhira, Marc A. Kastner, Ichiro Ide, Yasutomo Kawanishi, Takatsugu Hirayama, Keisuke Doman, Daisuke Deguchi, Hiroshi Murase

Proceedings of the 2020 International Conference on Multimedia Retrieval 頁： 306–310 2020年10月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Imageability is a concept from Psycholinguistics quantizing the human perception of words. However, existing datasets are created through subjective experiments and are thus very small. Therefore, methods to automatically estimate the imageability can be helpful. For an accurate automatic imageability estimation, we extend the idea of a psychological hypothesis called Dual-Coding Theory, that discusses the connection of our perception towards visual information
and language information, and also focus on the relationship between the pronunciation of a word and its imageability. In this research, we propose a method to estimate imageability of words
using both visual and language features extracted from corresponding data. For the estimation, we use visual features extracted from low- and high-level image features, and language features extracted from textual features and phonetic features of words. Evaluations how that our proposed method can estimate imageability more accurately than comparative methods, implying the contribution of each feature to the imageability.

DOI： 10.1145/3372278.3390731

Modeling Eye-Gaze Behavior of Electric Wheelchair Drivers via Inverse Reinforcement Learning 査読有り

Yamato Maekawa, Naoki Akai, Takatsugu Hirayama, Luis Yoichi Morales, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase

Proceedings of 2020 IEEE International Conference on Intelligent Transportation Systems 頁： 158 - 164 2020年9月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

It is intuitively obvious that eye-gaze behaviors of experienced drivers are different from those of novice drivers. However, it is not easy to understand the difference in their behavior quantitatively. In this work, we present an explainable eye-gaze behavior modeling method for electric wheelchair drivers based on Inverse Reinforcement Learning (IRL). We first create feature maps that represent risk factors during driving. These feature maps are able to represent not only to what but also from where drivers pay attention. IRL uses the feature maps to learn the reward representing the eye-gaze behaviors and allows us to see important features via the automatic acquisition of the reward. Through analysis of the learned model, we show quantitative evidence that eye-gaze behaviors of experienced drivers are better-balanced by paying attention to multiple risks.

DOI： 10.1109/ITSC45102.2020.9294255

Estimating the imageability of words by mining visual characteristics from crawled image data 査読有り

Marc Aurel Kastner, Ichiro Ide, Frank Nack, Yasutomo Kawanishi, Takatsugu Hirayama, Daisuke Deguchi, Hiroshi Murase

Multimedia Tools and Applications 79 巻 ( 25 ) 頁： 18167 - 18199 2020年7月

Performance Boost of Attribute-aware Semantic Segmentation via Data Augmentation for Driver Assistance 査読有り

Mahmud Dwi Sulistiyo, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Takatsugu Hirayama, Hiroshi Murase

頁： 293 - 298 2020年6月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This paper is an extension of our work in developing an attribute-aware semantic segmentation method which focuses on pedestrian understanding in a traffic scene. Recently, the trending topic of semantic segmentation has been expanded to be able to collaborate with the object’s attributes recognition task; Here, it refers to recognizing a pedestrian’s body orientation. The attribute-aware semantic segmentation can be more beneficial for driver assistance compared to the conventional semantic segmentation because it can provide a more informative output to the system. In this paper, we conduct a study of the data augmentation usage as an effort to enhance the performance of the attribute-aware semantic segmentation task. The experiments show that the proposed method in augmenting the training data is able to improve the model’s performance. We also demonstrate some of qualitative results and discuss the benefits to a driver assistance system.

DOI： 10.1109/ICoICT49345.2020.9166219

車両部位検出に基づく回帰を利用した車両台数の推定査読有り

新村文郷, 川西康友, 出口大輔, 井手一郎, 村瀬洋

知能と情報 32 巻 ( 3 ) 頁： 705 - 712 2020年6月

Simultaneous Image Matching for Person Re-identification via the Stable Marriage Algorithm 査読有り

Nik Mohd Zarifie Hashim, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEEJ Transactions ON Electrical AND Electronic Engineering 15 巻 ( 6 ) 頁： 909 - 917 2020年4月

SOANets: Encoder-Decoder based Skeleton Orientation Alignment Network for White Cane User Recognition from 2D Human Skeleton Sequence 査読有り

Naoki Nishida, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Jun Piao

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2020 5 巻頁： 435 - 443 2020年2月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

In recent years, various facilities have been deployed to support visually impaired people. However, accidents caused by visual disabilities still occur. In this paper, to support the visually impaired people in public areas, we aim to identify whether a pedestrian image sequence obtained by a surveillance camera indicates the presence of a white cane user by analyzing the temporal transition of a human skeleton represented as 2D coordinates. Our previously proposed method aligns the orientation of the human skeletons to various orientations and identifies a white cane user from the corresponding sequences, relying on multiple classifiers related to each orientation. The method employs an exemplar-based approach to perform the alignment. However, it heavily depends on the number of exemplars and consumes excessive memory. In this paper, we propose a method to align 2D human skeleton representation sequence to various orientations using the proposed Skeleton Orientation Alignment Networks (SOANets) based on an encoder-decoder model. Using SOANets, we can obtain 2D skeleton representation sequences aligned to various orientations, extract richer skeleton features, and recognize white cane users accurately. We conducted an experiment to confirm that the
proposed method improves the recognition rate by 16%, compared to the method that does not use the skeleton
orientation alignment.

LMS経由で手書きレポートを返却するWebサービス「かみレポ」の開発・評価査読有り

大平茂輝, 清谷竣也, 伊藤瑠哉, 岡本康佑, 谷川右京, 出口大輔, 戸田智基

情報処理学会論文誌：教育とコンピュータ（TCE） 6 巻 ( 1 ) 頁： 52 - 68 2020年2月

記述言語：日本語掲載種別：研究論文（学術雑誌）

高等教育における学習支援システム（LMS: Learning Management System）の重要性は年々高まっており，授業改善や学習分析などさまざまな目的のために活用されている．一方で，数式の記述や作図を必要とする授業や演習，試験など，紙媒体を主体的に扱う教育・学習環境は依然として多く残っており，それらの活動を支援するシステムが強く求められている．そこで本研究では，LMSと連携して手書きレポートを返却するWebサービスの実現を目指し，2016年度から「かみレポ」システムの開発を進めている．本稿では，半年間の試験運用時に実施した手書き数字の認識性能に関する評価と，運用開始後の1年6カ月間の運用評価をもとに，かみレポサービスの有効性と課題，今後の展望についてまとめる．

Occlusion-Aware Skeleton Trajectory Representation for Abnormal Behavior Detection 査読有り

Onur Temuroglu, Yasutomo Kawanishi, Daisuke Deguchi, Takatsugu Hirayama, Ichiro Ide, Hiroshi Murase, Mayuu Iwasaki, Atsushi Tsukada

2020年2月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Surveillance cameras are expected to play a large role in the development of ITS technologies. They can be used to detect abnormally behaving individuals which can then be reported to drivers nearby. There are multiple works that tackle the problem of abnormal behavior detection. However, most of these works make use of appearance features which have redundant information and are susceptible to noise. While there are also works that make use of pose skeleton representation, they do not consider well how to handle cases with occlusions, which can occur due to the simple reason of pedestrian orientation preventing some joints from appearing in the frame clearly. In this paper, we propose a skeleton trajectory representation that enables handling of occlusions. We also propose a framework for pedestrian abnormal behavior detection that uses the proposed representation and detect relatively hard-to-notice anomalies such as drunk walking. The experiments we conducted show that our method outperforms other representation methods.

Browsing Visual Sentiment Datasets Using Psycholinguistic Groundings

Marc Aurel Kastner, Ichiro Ide, Yasutomo Kawanishi, Takatsugu Hirayama, Daisuke Deguchi, Hiroshi Murase

Lecture Note in Computer Science 11962 巻頁： 697 - 702 2020年1月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Recent multimedia applications commonly use text and imagery from Social Media for tasks related to sentiment research. As such, there are various image datasets for sentiment research for popular classification tasks. However, there has been little research regarding the relationship between the sentiment of images and its annotations from a multi-modal standpoint. In this demonstration, we built a tool to visualize psycholinguistic groundings for a sentiment dataset. For each image, individual psycholinguistic ratings are computed from the image's metadata. A sentiment-psycholinguistic spatial embedding is computed to show a clustering of images across different classes close to human perception. Our interactive browsing tool can visualize the data in various ways, highlighting different psycholinguistic groundings with heatmaps.

DOI： 10.3758/s13428-018-1099-3

More-Natural Mimetic Words Generation for Fine-Grained Gait Description 査読有り

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

Lecture Note in Computer Science 11962 巻頁： 214 - 225 2020年1月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

A mimetic word is used to verbally express the manner of a phenomenon intuitively. The Japanese language is known to have a greater number of mimetic words in its vocabulary than most other languages. Especially, since human gaits are one of the most commonly represented behavior by mimetic words in the language, we consider that it should be suitable for labels of fine-grained gait recognition. In addition, Japanese mimetic words have a more decomposable structure than these in other languages such as English. So it is said that they have sound-symbolism and their phonemes are strongly related to the impressions of various phenomena. Thanks to this, native Japanese speakers can express their impressions on them briefly and intuitively using various mimetic words. Our previous work proposed a framework to convert the body-parts movements to an arbitrary mimetic word by a regression model. The framework introduced a phonetic space" based on sound-symbolism, and it enabled fine-grained gait description using the generated mimetic words consisting of an arbitrary combination of phonemes. However, this method did not consider the "naturalness" of the description. Thus, in this paper, we propose an improved mimetic word generation module considering its naturalness, and update the description framework. Here, we define the co-occurrence frequency of phonemes composing a mimetic word as the naturalness. To investigate the co-occurrence frequency, we collected many mimetic words through a subjective experiment. As a result of evaluation experiments, we confirmed that the proposed module could describe gaits with more natural mimetic words while maintaining the description accuracy."

DOI： 10.1007/978-3-030-37734-2_18

Attribute-Aware Loss Function for Accurate Semantic Segmentation Considering the Pedestrian Orientations 査読有り

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

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E103-A 巻 ( 1 ) 頁： 231 - 242 2020年1月

記述言語：英語掲載種別：研究論文（学術雑誌）

Numerous applications such as autonomous driving, satellite imagery sensing, and biomedical imaging use computer vision as an important tool for perception tasks. For Intelligent Transportation Systems (ITS), it is required to precisely recognize and locate scenes in sensor data. Semantic segmentation is one of computer vision methods intended to perform such tasks. However, the existing semantic segmentation tasks label each pixel with a single object’s class. Recognizing object attributes, e.g., pedestrian orientation, will be more informative and help for a better scene understanding. Thus, we propose a method to perform semantic segmentation with pedestrian attribute recognition simultaneously. We introduce an attribute-aware loss function that can be applied to an arbitrary base model. Furthermore, a re-annotation to the existing Cityscapes dataset enriches the ground-truth labels by annotating the attributes of pedestrian orientation. We implement the proposed method and compare the experimental results with others. The attribute-aware semantic segmentation shows the ability to outperform baseline methods both in the traditional object segmentation task and the expanded attribute detection task.

DOI： 10.1587/transfun.2019TSP0001

インテリジェントヘッドライトシステムにおける運転者による歩行者認知に効果的な点滅光照射パターンの分析査読有り

前田高志, 平山高嗣, 川西康友, 出口大輔, 井手一郎, 村瀬洋

精密工学会誌 85 巻 ( 12 ) 頁： 1157 - 1162 2019年12月

入力可変長PointNetによる自転車認識査読有り

山本大貴, 新村文郷, 出口大輔, 川西康友, 井手一郎, 加藤一樹, 村瀬洋

精密工学会誌 85 巻 ( 12 ) 頁： 1117 - 1126 2019年12月

記述言語：日本語掲載種別：研究論文（学術雑誌）

近年，自動運転の実用化を目指して，距離センサであるLIDAR（Light Detection and
Ranging）を用いた周辺環境認識に関する研究が活発に行われている．これらの研究
の多くは自動車や歩行者の認識を目的としているが，一般道における自動運転を実用
化するためには，自転車の認識も重要である．自転車は歩行者に比べて移動速度が速
いため，衝突の危険性が高く，安全のために迅速かつ正確な認識が求められる．近年
，3D点群データを直接扱うDNN手法としてPointNetが提案されており，高い物体認識
精度が得られることが示されている．しかし，PointNetは入力点数が固定であるとい
う制約があり，認識対象物体の点数が大きく変化する場合にそのまま適用することは
難しい．そこで本論文では，LIDARにより計測された自転車の認識精度の向上を目的
とし，自転車の点群の分布形状を保持したダウンサンプリングとLIDARの測定精度を
考慮したアップサンプリングを行うサンプリングモジュールを提案する．ダウンサン
プリングでは，点群が低密度な部分の計測点が保存されるようサンプリングを行うこ
とで，自転車の点群の分布形状を保持したサンプリングを可能とする．アップサンプ
リングでは，LIDARの計測時に生じる誤差を考慮し，対象までの距離に応じて点群に
摂動を加えることで，点群の分布形状を大きく変化させることなく点群の密度の増加
を図る．この提案サンプリングモジュールの導入により，PointNetの入力の可変長化
を行う．KITTIデータセットを用いて評価実験を行い，提案手法によって認識精度が向
上することを確認した．

DOI： 10.2493/jjspe.85.1117

Scene-Adaptive Driving Area Prediction based on Automatic Label Acquisition from Driving Information 査読有り

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

Proceedings of the 5th IAPR Asian Conference on Pattern Recognition (ACPR2019) 2019年11月

Semantic Segmentation of Railway Images Considering Temporal Continuity 査読有り

Yuki Furitsu, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase, Hiroki Mukoujima, Nozomi Nagamine

Proceedings of the 5th IAPR Asian Conference on Pattern Recognition (ACPR2019) 2019年11月

An Analysis of How Driver Experience Affects Eye-Gaze Behavior for Robotic Wheelchair Operation 査読有り

Yamato Maekawa, Naoki Akai, Takatsugu Hirayama, Luis Yoichi Morales, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase

Proceedings of 2019 IEEE International Conference on Computer Vision (ICCV) Workshops 頁： 4443 - 4451 2019年11月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Drivers obtain information on surrounding environment using their eyesights. Experienced eye-gaze behavior is needed when driving at places where multiple risks exist to prepare for and avoid them. In this work, we analyze the change in eye-gaze behavior in such situations while a driver gains experience on the operation of a robotic wheelchair. Accurate distance information in the traffic environment is important to analyze the eye-gaze behavior. However, almost all previous works analyze eye-gaze behavior in a 2D environment, so they could not obtain accurate distance information. For this reason, we analyze eye-gaze behavior in 3D space. Concretely, we developed a novel eye-gaze behavior analysis platform based on a robotic wheelchair and estimated the driver's attention in 3D space. We try to analyze the eye-gaze behavior considering a useful field-of-view in 3D space based on the distance information instead of only the fixation point to investigate the objects that a driver implicitly pays attention to and from where s/he focuses on them. Results show that novice drivers pay attention to a single risk at a time. In contrast, they pay more attention to multiple risks simultaneously as they gain experience. Additionally, we discuss what features are effective to model the eye-gaze behavior based on the results.

DOI： 10.1109/ICCVW.2019.00545

Exemplar-based Pseudo-Viewpoint Rotation for White-Cane User Recognition from a 2D Human Pose Sequence 査読有り

Naoki Nishida, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Jun Piao

2019年9月

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