Faculty Profiles - Deguchi Daisuke

Presentations -

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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

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

Single Camera Vehicle Localization Using SURF Scale and Dynamic Time Warping International conference

David Robert Wong, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

2014 the IEEE Intelligent Vehicles Symposium (IV2014) 2014.6.10

Event date： 2014

Language：English Presentation type：Poster presentation

Venue：Dearborn, Michigan, USA

Vehicle ego-localization is an essential process for many driver assistance and autonomous driving systems. The traditional solution of GPS localization is often unreliable in urban environments where tall buildings can cause shadowing of the satellite signal and multipath propagation. Typical visual feature based localization methods rely on calculation of the fundamental matrix which can be unstable when the baseline is small.
In this paper we propose a novel method which uses the scale of matched SURF image features and Dynamic Time Warping to perform stable localization. By comparing SURF feature scales between input images and a pre-constructed database, stable localization is achieved without the need to calculate the fundamental matrix. In addition, 3D information is added to the database feature points in order to perform lateral localization, and therefore lane recognition.
From experimental data captured from real traffic environments, we show how the proposed system can provide high localization accuracy relative to an image database, and can also perform lateral localization to recognize the vehicle's current lane.

Estimation of Traffic Sign Visibility Considering Local and Global Features in a Driving Environment International conference

Keisuke Doman, Daisuke Deguchi, Tomokazu Takahashi, Yoshito Mekada, Ichiro Ide, Hiroshi Murase, Utsushi Sakai

2014 IEEE Intelligent Vehicles Symposium (IV2014) 2014.6.9

Event date： 2014

Language：English Presentation type：Poster presentation

Venue：Dearborn, Michigan, USA

This paper proposes a camera-based visibility estimation method for a traffic sign.
The visibility here indicates how a visual target is easy to be detected and recognized by a human driver (not a machine).
This research aims at realizing a nuisance-free driver assistance system which sorts out information depending on the visibility of a visual target, in order to prevent driver distraction.
Our previous study on estimating the visibility of a traffic sign considered only the effect of the local region around a target, assuming the situation that a driver's gaze is around it.
The proposed method integrates both the local features and global features in a driving environment without such an assumption.
The global features evaluate the positional relationships between traffic signs and the appearance around the fixation point of a driver's gaze, which considers the effect of the driver's entire field of view.
Experimental results showed the effectiveness of incorporating the global features for estimating the visibility of a traffic sign.

Estimation of a politician's stance based on his/her posts to microblogs

Hiromichi Iwai, Keisuke Doman, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

2014.5.12

Estimation of the Representative Story Transition in a Chronological Semantic Structure of News Topics International conference

Kosuke Kato, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

4th International Conference on Multimedia Retrieval (ICMR2014) 2014.4.4

Event date： 2014

Language：English Presentation type：Poster presentation

Venue：Glasgow University

It is important to track the flow of topics to thoroughly understand the contents. Accordingly, a method that structures the chronological semantic relations between news stories, namely a 'topic thread structure' has been proposed.It allows the comprehensive understanding of a topic by chronologically tracking stories one by one from the initial story. However, this task imposes a user to watch many stories when it contains various sub-topics. Thus, we propose method that estimates the representative story transition in a topic thread structure. In the proposed method, features obtained from a story and those from the topic thread structure are used for the estimation. We confirmed the effectiveness of the proposed method by comparing the results obtained from the proposed method to the ground truth obtained from votes in a subjective experiment. However, this task imposes a user to watch many stories when it contains various sub-topics. Thus, we propose method that estimates the representative story transition in a topic thread structure. In the proposed method, features obtained from a story and those from the topic thread structure are used for the estimation. We confirmed the effectiveness of the proposed method by comparing the results obtained from the proposed method to the ground truth obtained from votes in a subjective experiment

A Preliminary Study on the Removal of Moving Objects in a Frontal In-vehicle Camera Image Sequence

Toru Kotsuka, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase