Faculty Profiles - Deguchi Daisuke

Presentations -

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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

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.

A Preliminary Study on the Relationship between Gaze Direction and Head Pose with Respect to Body

Nobuhiro Funatsu, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

2012 IEICE General Conference 2012.3.21

A Study on Facial Image Search Using Attributes Based on Impression

Yasunori Ozaki, Daisuke Deguchi, Tomokazu Takahashi, Ichiro Ide, Hiroshi Murase

2012 IEICE General Conference 2012.3.21

Super-resolution using facial pose variation

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

2012 IEICE General Conference 2012.3.21

A study on region-based matching for scene duplicate detection from news videos

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

IEICE Technical Report (MVE) 2012.3.12

Digital Camera-Working for Cooking Videos Based on the Determination of Target Areas According to the Cooking Process

Wataru Hyogo, Yasuhiro Hayashi, Masafumi Noda, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEICE Technical Report (MVE) 2012.3.12

Extraction of Speech Shots Focusing on Visual and Audio Features within and between Shots

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

IEICE Technical Report (MVE) 2012.3.12

Video retrieval by generic object recognition using spatio-temporal CoHOG features

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

Dynamic Image Processing for Real Application workshop (DIA2012) 2012.3.9

A Study on Image Features Considering Properties of Visual Search for Estimation of Pedestrian Detectability

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

Dynamic Image Processing for Real Application workshop (DIA2012) 2012.3.8

Prediction of driving behavior using gaze information and vehicle information

Tatsuki Kamisaka, Masafumi Noda, Yoshito Mekada, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Dynamic Image Processing for Real Application workshop (DIA2012) 2012.3.8

Integration of generative learning and multiple pose classifiers for pedestrian detection International conference

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

International Conference on Computer Vision Theory and Applications (VISAPP) 2012 2012.2.26

Event date： 2012

Language：English Presentation type：Oral presentation (general)

Venue：Rome, Italy

Recently, pedestrian detection from in-vehicle camera images is becoming an important technology in ITS (Intelligent Transportation System).
However, it is difficult to detect pedestrians stably due to the variety of their poses and their backgrounds.
To tackle this problem, we propose a method to detect various pedestrians from in-vehicle camera images by using multiple classifiers corresponding to various pedestrian pose classes.
Since pedestrians’ pose varies widely, it is difficult to construct a single classifier that can detect pedestrians with various poses stably.
Therefore, this paper constructs multiple classifiers optimized for variously posed pedestrians by classifying pedestrian images into multiple pose classes.
Also, to reduce the bias and the cost for preparing numerous pedestrian images for each pose class for learning, the proposed method employs a generative learning method.
Finally, the proposed method constructs multiple classifiers by using the synthesized pedestrian images.
Experimental results showed that the detection accuracy of the proposed method outperformed comparative methods, and we confirmed that the proposed method could detect variously posed pedestrians stably.

DOI： 10.5220/0003817305670572

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