Faculty Profiles - Deguchi Daisuke

Presentations -

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A Study on Event Detection in a Sports Game using Twitter

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

HCG Symposium 2012 2012.12.11

Estimation of the human performance for pedestrian detectability based on visual search and motion features International conference

Masashi Wakayama, Daisuke Deguchi, Keisuke Doman, Ichiro Ide, Hiroshi Murase, Yukimasa Tamatsu

21st IAPR International Conference on Pattern Recognition (ICPR2012) 2012.11.14

Event date： 2012

Language：English Presentation type：Poster presentation

Venue：Tsukuba, Japan

This paper proposes a method for estimating the human performance of pedestrian detectability from in-vehicle camera images in order to warn a driver of the positions of pedestrians in an appropriate timing. By introducing features related to visual search and motion of the target, the proposed method estimates the detectability of pedestrians accurately. Support Vector Regression (SVR) is used to estimate the detectability. Here, SVR is trained using features calculated by the proposed method with the ground truth obtained through experiments with human subjects. From experiments using in-vehicle camera images, we confirmed that the proposed features were effective to estimate the detectability of pedestrians.

Looking back on past PRMU Algorithm Contests

Daisuke Deguchi, Yoshinari Kameda, Itaru Kitahara, Kazuaki Kondo, Atsushi Shimada, Shinsaku Hiura

IEICE Technical Report (PRMU) 2012.9.3

A Study on Spatio-Temporal CoHOG Features for Recognition of Generic Objects in Video

Shogo Nakamura, Daisuke Deguchi, Tomokazu Takahashi, Ichiro Ide, Hiroshi Murase

IEICE Technical Report (PRMU) 2011.12.15

PRMU Algorithm Contest 2010 "Lock on targets! - object tracking -"

Daisuke Deguchi, Hiroshi Murase, Kazuaki Umeda, Masataka Saito, Kenichiro Fuji, Shingo Yamashita, Yuki Suetsugu, Syota Takai, Ryuta Sato, Takuma Maruyama

IEICE Technical Report (PRMU) 2011.3

Pedestrian Detection from In-vehicle Camera Images using Scene Context Information

Daisuke Hamada, Daisuke Deguchi, Tomokazu Takahashi, Yoshito Mekada, Ichiro Ide, Hiroshi Murase

2011 IEICE General Conference 2011.3

Pedestrian Detection with Generative Learning Method Considering Varying Pose

Hidefumi Yoshida, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Kunihiro Goto, Yoshikatsu Kimura, Takashi Naito

2011 IEICE General Conference 2011.3

Person Re-identification at a Room Entrance by Adaptive Feature Selection

Takuya Honda, Daisuke Deguchi, Tomokazu Takahashi, Ichiro Ide, Hiroshi Murase

IEICE Technical Report (PRMU) 2011.3

Proposal of a Sight-seeing Map with Visual Classification and Presentation of Photos on the Web

Jiani Wang, Masafumi Noda, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEICE Technical Report (MVE) 2011.3

Scene Segmentation of a Wedding Party Video by Scenario-based Matching with Example Videos

Kazuki Sawai, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEICE Technical Report (MVE) 2011.3

Study on super-resolution under facial pose variation for low-resolution face recognition

Tomonari Yoshida, Daisuke Deguchi, Tomokazu Takahashi, Ichiro Ide, Hiroshi Murase

2011 IEICE General Conference 2011.3

Summarizing sports video by on-the-spot comments on Twitter

Takashi Kobayashi, Masafumi Noda, Daisuke Deguchi, Tomokazu Takahashi, Ichiro Ide, Hiroshi Murase

IEICE Technical Report (MVE) 2011.3

Summarizing sports video by on-the-spot comments on Twitter

Takashi Kobayashi, Masafumi Noda, Daisuke Deguchi, Tomokazu Takahashi, Ichiro Ide, Hiroshi Murase

2011.3

Video-based Generic Object Recognition by Combining Motion Features and BoF

Shogo Nakamura, Daisuke Deguchi, Tomokazu Takahashi, Ichiro Ide, Hiroshi Murase

IEICE Technical Report (PRMU) 2011.1.21

Quantification of the Visibility of Pedestrians Based on Integration of Local and Global Features

Masashi Wakayama, Keisuke Doman, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Yukimasa Tamatsu

IEICE Technical Report (PRMU) 2011.1

A study on a method for stable pedestrian detection against pose changes with generative learning

Hidefumi Yoshida, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Kunihiro Goto, Yoshikatsu Kimura, Takashi Naito

IEICE Technical Report (PRMU) 2011.5.20

Advertisements on Urban Structures by Augmented Reality

Hiroyuki Uchiyama, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Takahito Kawanishi, Kunio Kashino

Vision Engineering Workshop (ViEW) 2011 2011.12.9

Multi-frame super-resolution for low resolution QR-code recognition

Yuji Kato, Daisuke Deguchi, Tomokazu Takahashi, Ichiro Ide, Hiroshi Murase

Vision Engineering Workshop (ViEW) 2011 2011.12.9

Road-image Map Construction Using Low-resolution Aerial Image and In-vehicle Camera Images

Masafumi Noda, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Yoshiko Kojima, Takashi Naito

Vision Engineering Workshop (ViEW) 2011 2011.12.9

Detection of Inconsistency between Subject and Speaker based on the Co-occurrence of Lip Motion and Voice Towards Speech Scene Extraction from News Videos International conference

Shogo Kumagai, Keisuke Doman, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

2011 IEEE International Symposium on Multimedia (ISM2011) 2011.12.6

Event date： 2011

Language：English Presentation type：Oral presentation (general)

Venue：Dana Point, California USA

We propose a method to detect the inconsistency between a subject and the speaker for extracting speech scenes from news videos.
Speech scenes in news videos contain a wealth of multimedia information, and are valuable as archived material.
In order to extract speech scenes from news videos, there is an approach that uses the position and size of a face region.
However, it is difficult to extract them with only such approach, since news videos contain non-speech scenes where the speaker is not the subject, such as narrated scenes.
To solve this problem, we propose a method to discriminate between speech scenes and narrated scenes based on the co-occurrence between a subject's lip motion and the speaker's voice.
The proposed method uses lip shape and degree of lip opening as visual features representing a subject's lip motion, and uses voice volume and phoneme as audio feature representing a speaker's voice.
Then, the proposed method discriminates between speech scenes and narrated scenes based on the correlations of these features.
We report the results of experiments on videos captured in a laboratory condition and also on actual broadcast news videos.
Their results showed the effectiveness of our method and the feasibility of our research goal.

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