Faculty Profiles - Deguchi Daisuke

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A Feasibility Study of Human Cloud Sensing

Atsushi Shimada, Daisuke Deguchi, Kazuaki Kondo, Takuya Funatomi

2014 IEICE General Conference 2014.3.18

Accuracy improvement of pedestrian detection from in-vehicle camera images based on environment adaptation using location information

Daichi Suzuo, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Hiroyuki Ishida, Yoshiko Kojima

IEICE Technical Report (PRMU) 2014.3.13

Proposal of the weighted voting type co-training method for the construction of an accurate traffic sign detector

Yuji Kojima, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEICE Technical Report (PRMU) 2014.3.13

名古屋大学におけるSakaiの利用状況およびリーディング大学院の活用事例の紹介

中務孝広, 太田芳博, 田上奈緒, 大平茂輝, 後藤明史, 出口大輔, 森健策

Ja Sakai カンファレンス 2014.3.10

Event date： 2014

Language：Japanese Presentation type：Oral presentation (general)

Venue：名古屋大学

名古屋大学では，2010年度からSakaiによるNUCT（Nagoya University Collaboration and course Tools）を，講義における利用を主たる目的として全学向けに運用してきた．本稿では，Sakaiにおける利用状況等について報告するとともに，博士課程教育リーディングプログラムにおける教育学習支援システムとして，Sakaiを利活用した事例について紹介する．

A method to relate news stories to Web news articles for video structuring using SNS

Kosuke Kato, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

IEICE Technical Report (MVE) 2014.3.7

Scene Duplicate Detection from News Videos by Sub-Region-based Motion Matching of a Face

Haruka Kumagai, Keisuke Doman, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

IEICE Technical Report (MVE) 2014.3.7

A Study on the Estimation of Tastes of Cooking Recipes Based on Training Using Comments on Cooking Recipes as Supervisory Signals

Hiroki Matsunaga, Keisuke Doman, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

IEICE Technical Report (MVE) 2014.3.6

A study on memory-based regression for spatial people density estimation

Yoshimune Tabuchi, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Takayuki Kurozumi, Kunio Kashino

IEICE Technical Report (PRMU) 2014.2.13

Proposal of Human Cloud Sensing

Atsushi Shimada, Daisuke Deguchi, Kazuaki Kondo, Takuya Funatomi

IEICE Technical Report (PRMU) 2014.2.13

The 1st Feasibility Study toward Human Cloud Sensing

Daisuke Deguchi, Kazuaki Kondo, Takuya Funatomi, Atsushi Shimada

IEICE Technical Report (PRMU) 2014.2.13

The 2nd Feasibility Study toward Human Cloud Sensing

Kazuaki Kondo, Takuya Funatomi, Atsushi Shimada, Daisuke Deguchi

IEICE Technical Report (PRMU) 2014.2.13

Can a Human be a Sensor? International conference

Atsushi Shimada, Daisuke Deguchi, Kazuaki Kondo, Takuya Funatomi

20th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2014) 2014.2.5

Event date： 2014

Language：English Presentation type：Poster presentation

Venue：Okinawa National College of Technology

This paper discusses a novel sensing strategy to retrieve real-world information. Instead of using sensor devices such as cameras, microphones and so on, the proposed approach involves people in real-world sensing to acquire requested information as accurately and quickly as possible. We call the proposed sensing strategy as Human Cloud Sensing (HCS)". In this paper, we introduce the concept of HCS and report some experimental results which were conducted to investigate the feasibility of "Can a human be a sensor?"."

Environment adaptive pedestrian detection using in-vehicle camera and GPS International conference

Daichi Suzuo, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Hiroyuki Ishida, Yoshiko Kojima

International Conference on Computer Vision Theory and Applications (VISAPP) 2014 2014.1.7

Event date： 2014

Language：English Presentation type：Oral presentation (general)

Venue：Lisbon

In recent years, accurate pedestrian detection from in-vehicle camera images is focused to develop a safety driving assistance system. Currently, successful methods are based on statistical learning. However, in such methods, it is necessary to prepare a large amount of training images. Thus, the decrease in the number of training images degrades the detection accuracy. That is, in driving environments with few or no training images, it is difficult to detect pedestrians accurately. Therefore, we propose an approach that collects training images automatically to build classifiers for various driving environments. This is expected to realize highly accurate pedestrian detection by using an appropriate classifier corresponding to the current location. The proposed method consists of three steps; Classification of driving scenes, collection of non-pedestrian images and training of classifiers for each scene class, and associating a scene-class-specific classifier with GPS location information. Through experiments, we confirmed the effectiveness of the method compared to baseline methods.

DOI： 10.5220/0004677003540361

Exemplar-Based Human Body Super-Resolution for Surveillance Camera Systems International conference

Kento Nishibori, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

International Conference on Computer Vision Theory and Applications (VISAPP) 2014 2014.1.6

Event date： 2014

Language：English Presentation type：Poster presentation

Venue：Lisbon

In this paper, we propose an exemplar-based super-resolution method applied to a human body in a surveillance video. Since persons are usually captured as low-resolution images by a video surveillance system, it is sometimes necessary to perform detection and identification of persons from not only a human face but also from the human body appearance. The super-resolution for a human body image is difficult because the appearances of person images vary according to the color of clothing and the posture of persons. Thus, we focus on the high-frequency components that could restore the lost high-frequency components of the low-resolution image regardless to the variation of the clothing. Therefore, the purpose of the work presented in this paper is to apply the exemplar-based super-resolution using high-frequency components for a low-resolution human body image to generate a high-resolution human body image so that both computer systems and humans can identify persons more accurately. As a result of experiments, we confirmed the effectiveness of the proposed super-resolution method.

DOI： 10.5220/0004686101150121

Sakaiにおけるi18nとl10n ～現状と課題～

出口大輔

大学ICT推進協議会（AXIES） 2014.12.11

名古屋大学における情報サービスの運用改善を目的としたシステムトラブル対応DBの構築

瀬川午直, 出口大輔, 渥美紀寿, 加藤芳秀, 嶋田創, 荻野正雄, 小川泰弘, 大野誠寛, 川田良文, 山田一成

大学ICT推進協議会（AXIES） 2014.12.12

車両部位検出を利用した回帰による車両台数推定

新村文郷, 出口大輔, 井手一郎, 村瀬洋

画像センシングシンポジウム(SSII) 2014.6.13

Event date： 2014

Language：Japanese Presentation type：Poster presentation

Venue：パシフィコ横浜アネックスホール

安全運転支援において，周囲の道路状況を理解することは重要である．自車周囲を走行する他車両の台数の把握は，道路状況の理解に役立つ重要な情報と考えられる．本発表では，車載カメラ画像中から車両の台数を推定するため，車両部位検出を利用した回帰による車両台数推定手法を提案する．車両を検出して台数を数える手法では，車両のオクルージョンにより検出精度が低下する問題がある．この問題を回避するため，提案手法では車両部位を検出して台数を数えるアプローチを用いる．車両の部位数から台数を推定する場合，車両の台数と部位数の関係は複雑であり，部位数から車両台数を一意に求めることは難しい．そこで，回帰を導入することにより，推定誤差の低減を図る．提案手法の有効性を確認するため，実環境で撮影した車載カメラ画像に対して提案手法を適用して車両台数を推定し，推定精度を評価した．評価の結果，提案手法は平均絶対誤差1.11 台の精度で車両台数を推定可能なことを確認した．

Scene duplicate detection from news videos using image-audio matching focusing on human faces International conference

Haruka Kumagai, Keisuke Doman, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

16th IEEE International Symposium on Multimedia (ISM2014) 2014.12.10

A Proposal of Encoding Marker which is Robust against Blur and easy to Detect

Norimasa Kobori, Daisuke Deguchi, Tomokazu Takahashi, Ichiro Ide, Hiroshi Murase

IEICE Technical Report (IE) 2014.12.4

Event detection based on twitter enthusiasm degree for generating a sports highlight video International conference

Keisuke Doman, Taishi Tomita, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

ACM International Multimedia Conference (ACM-MM) 2014.11.5

Event date： 2014

Language：English Presentation type：Poster presentation

Venue：Orlando, Florida, USA

This paper presents a Twitter-based event detection method based on Twitter Enthusiasm Degrees (TED)" toward generating a highlight video of a sports game. Existing methods not only depend on both languages and sports types but also often falsely detect non-target events. In contrast, the proposed method detects sports events using TEDs calculated from several kinds of string features independent of languages and sports. We applied the proposed method to actual sports games, and compared the detected events with the events present in broadcasted highlight videos, and confirmed the effectiveness and the language and sports type independencies of the proposed method."

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