Faculty Profiles - Deguchi Daisuke

Presentations -

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Scene dependent classifiers for pedestrian detection International conference

Hidefumi Yoshida, Daichi Suzuo, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

International Joint Workshop on Advanced Sensing / Visual Attention and Interaction (ASVAI2013) 2013.11.5

Automatic authoring of a domestic cooking video based on the description of cooking instructions International conference

Yasuhiro Hayashi, Keisuke Doman, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

5th International Workshop on Multimedia for Cooking and Eating Activities (CEA'13) 2013.10.21

Blur-invariant Traffic Sign Recognition Using Compact Local Phase Quantization International conference

Saleh Aly, Daisuke Deguchi, Hiroshi Murase

2013 IEEE Conference on Intelligent Transportation Systems (ITSC2013) 2013.10.8

Construction of a traffic sign detector based on voting type co-training International conference

Yuji Kojima, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

2013 IEEE Conference on Intelligent Transportation Systems (ITSC2013) 2013.10.8

A study on people density estimation using multiple cameras

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

IEICE Technical Report (PRMU) 2013.10.3

Prediction of Pedestrian Detectability for Drivers by Image Processing and its Driver Adaptation International conference

Ryunosuke Tanishige, Daisuke Deguchi, Keisuke Doman, Yoshito Mekada, Ichiro Ide, Hiroshi Murase, Naoki Nitanda

6th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems (DSP2013) 2013.9.30

Event date： 2013

Language：English Presentation type：Oral presentation (general)

Venue：Korea University, Seoul, Korea

In recent years, advances in pedestrian detection technology have resulted in the development of driving assistance systems that notify the drivers of the presence of pedestrians. However, warning of all of the presence of pedestrians would confuse the driver. Therefore, the driver should only be notified of the less detectable pedestrians to avoid confusion. To achieve this, it is necessary to develop a method to predict the driver’s perception performance of pedestrian detectability. This paper proposes a method that predicts the pedestrian detectability considering the difference between individual drivers. The proposed method constructs a predictor specific to each driver, in order to predict the pedestrian detectability precisely. To obtain the ground truth of the pedestrian detectability, we conducted an experiment by human subjects using images from an in-vehicle camera including pedestrians. From the comparison between the output of the proposed method and the actual detectability, we confirmed that the proposed method significantly reduces the prediction error in comparison with the existing methods.

Scene Duplicate Detection from News Videos Using Visual Features and Audio Features

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

IEICE Technical Report (MVE) 2013.9.26

A study on a driving environment adaptive pedestrian detection leveraging location information

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

Tokai-Section Joint Conference on Electrical and Related Engineering 2013 2013.9.25

Localization of point heat sources using a low resolution infrared sensor array

Takashi Hosono, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEICE Technical Report (PRMU) 2013.12.12

A preliminary study on the prediction of pedestrian's detectability considering drivers' characteristics

Ryunosuke Tanishige, Daisuke Deguchi, Keisuke Doman, Yoshito Mekada, Ichiro Ide, Hiroshi Murase, Utsushi Sakai

IEICE Technical Report (PRMU) 2013.12.12

Sakaiを中心とした教育基盤構築の現状と今後の展開

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

大学ICT推進協議会（AXIES） 2012.12.17

A Collaborative Report on The 13th Sakai Conference

Yuji Tokiwa, Makoto Miyazaki, Ryuichi Matsuba, Daisuke Deguchi, Shoji Kajita

IPSJ SIG Technical Report (CLE) 2012.11.3

Subtraction-Based Forward Obstacle Detection Using Illumination Insensitive Feature for Driving-Support International conference

Haruya Kyutoku, Daisuke Deguchi, Tomokazu Takahashi, Yoshito Mekada, Ichiro Ide, Hiroshi Murase

Computer Vision in Vehicle Technology: From Earth to Mars (CVVT2012) 2012.10.13

Event date： 2012

Language：English Presentation type：Oral presentation (general)

Venue：Firenze, Italy

This paper proposes a method for detecting general obstacles on a road by subtracting present and past in-vehicle camera images.
The image-subtraction-based object detection approach can be applied to detect any kind of obstacles although the existing learning-based methods detect only specific obstacles.
To detect general obstacles, the proposed method first computes a frame-by-frame correspondence between the present and the past in-vehicle camera image sequences, and then registrates road surfaces between the frames.
Finally, obstacles are detected by applying image subtraction to the registrated road surface regions with an illumination insensitive feature for robust detection.
Experiments were conducted by using several image sequences captured by an actual in-vehicle camera to confirm the effectiveness of the proposed method.
The experimental results shows that the proposed method can detect general obstacles accurately at a distance enough to avoid them safely even in situations with different illuminations.

DOI： 10.1007/978-3-642-33868-7_51

Visibility Estimation of Traffic Signals under Rainy Weather Conditions for Smart Driving Support International conference

Ryuhei Sato, Keisuke Doman, Daisuke Deguchi, Yoshito Mekada, Ichiro Ide, Hiroshi Murase, Yukimasa Tamatsu

2012 IEEE Conference on Intelligent Transportation Systems (ITSC2012) 2012.9.19

Event date： 2012

Language：English Presentation type：Oral presentation (general)

Venue：Anchorage, Alaska, USA,

The aim of this work is to support a driver by
notifying the information of traffic signals in accordance with
their visibility. To avoid traffic accidents, the driver should
detect and recognize surrounding objects, especially traffic
signals. However, when driving a vehicle under rainy weather
conditions, it is difficult for drivers to detect or to recognize
objects existing in the road environment in comparison with fine
weather conditions. Therefore, this paper proposes a method
for estimating the visibility of traffic signals for drivers under
rainy weather conditions by image processing. The proposed
method is based on the concept of visual noise known in the
field of cognitive science, and extracts two types of visual noise
features which ware considered that they affect the visibility of
traffic signals. We expect to improve the accuracy of visibility
estimation by combining the visual noise features with the
texture feature introduced in a previous work. Experimental
results showed that the proposed method could estimate the
visibility of traffic signals more accurately under rainy weather
conditions.

DOI： 10.1109/ITSC.2012.6338838

A preliminary study on a pedestrian detector adaptive to the driving environment using an in-vehicle camera

Daichi Suzuo, Hidefumi Yoshida, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Takashi Machida, Yoshiko Kojima

IEICE Technical Report (PRMU) 2012.9.3

Virtual View Generation using Clustering based Local View Transition Model International conference

Xi Li, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

ACCV Workshop on Face Analysis: The Intersection of Computer Vision and Human Perception 2012.11.6

Event date： 2012

Language：English Presentation type：Oral presentation (general)

Venue：Deajeon Convention Center, Daejeon, Korea

This paper presents an approach for realistic virtual view generation using appearance clustering based local view transition model, with its target application on cross-pose face recognition. Previously, the traditional global pattern based view transition model (VTM) method was extended to its local version called LVTM, which learns the linear transformation of pixel values between frontal and non-frontal image pairs using partial image in a small region for each location, rather than transforming the entire image pattern. In this paper, we show that the accuracy of the appearance transition model and the recognition rate can be further improved by better exploiting the inherent linear relationship between frontal-nonfrontal face image patch pairs. For each specific location, instead of learning a common transformation as in the LVTM, the corresponding local patches are first clustered based on appearance similarity distance metric and then the transition models are learned separately for each cluster. In the testing stage, each local patch for the input non-frontal probe image is transformed using the learned local view transition model corresponding to the most visually similar cluster. The experimental results on a real-world face dataset demonstrated the superiority of the proposed method in terms of recognition rate.

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

Detection and classification of repetitious human motions combining shift variant and invariant features International conference

Ichiro Ide, Taku Kuhara, Daisuke Deguchi, Tomokazu Takahashi, Hiroshi Murase

3rd International Conference on Emerging Security Technologies (EST2012) 2012.9

多数決型co-trainingによる道路標識検出器の構築に関する検討

児島勇司, 出口大輔, 井手一郎, 村瀬洋

画像の認識・理解シンポジウム(MIRU) 2012.8.8

顔と体の姿勢情報を利用した人物動画像からの視線方向推定に関する検討

船津暢宏, 高橋友和, 出口大輔, 井手一郎, 村瀬洋

画像の認識・理解シンポジウム(MIRU) 2012.8.8

Event date： 2012

Language：Japanese Presentation type：Poster presentation

Venue：福岡国際会議場

カメラを用いて人物の視線方向を推定する技術が注目されている．これに関して，低解像度や遮蔽などが原因で画像中から人物の目領域を検出するのが困難な場合に，顔の向きを視線方向の代わりとして利用する研究がある．しかしながら，実際には顔の向きの他にも，体の向きやそれらの時間変化，人物がおかれている状況なども視線方向に影響を与える要因として考えられる．そこで本研究では，これらの情報を利用して視線方向をより高精度に推定することを目的としている．発表ではそれらの要因のうち，特に顔の向きと体の向き着目し，体に対する顔の向きと視線方向との関係を被験者実験により調査した結果について報告する．さらに，その調査結果に基づいた視線方向推定を行い，その推定誤差を評価した．その結果，体に対する顔の向きを利用した場合，顔の向きを視線方向として利用する場合よりも推定誤差が平均 14.9 度小さくなることを確認した．

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