研究者詳細 - 出口　大輔

論文 - 出口　大輔

分割表示 253 件中 141 - 160 件目／全件表示 >>

Estimation of Human Orientation using Coaxial RGB-Depth Images 査読有り

Fumito Shinmura, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Hironobu Fujiyoshi

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2015 頁： 113 - 120 2015年3月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Estimation of human orientation contributes to improving the accuracy of human behavior recognition. However,
estimation of human orientation is a challenging task because of the variable appearance of the human
body. The wide variety of poses, sizes and clothes combined with a complicated background degrades the
estimation accuracy. Therefore, we propose a method for estimating human orientation using coaxial RGBDepth
images. This paper proposes Depth Weighted Histogram of Oriented Gradients (DWHOG) feature
calculated from RGB and depth images. By using a depth image, the outline of a human body and the texture
of a background can be easily distinguished. In the proposed method, a region having a large depth gradient
is given a large weight. Therefore, features at the outline of the human body are enhanced, allowing robust
estimation even with complex backgrounds. In order to combine RGB and depth images, we utilize a newly
available single-chip RGB-ToF camera, which can capture both RGB and depth images taken along the same
optical axis. We experimentally confirmed that the proposed method can estimate human orientation robustly
to complex backgrounds, compared to a method using conventional HOG features.

DOI： 10.5220/0005305301130120

Scene duplicate detection from news videos using image-audio matching focusing on human faces 査読有り

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

Proceedings of the 16th IEEE International Symposium on Multimedia (ISM2014) 頁： 71 - 77 2014年12月

Event detection based on twitter enthusiasm degree for generating a sports highlight video 査読有り

道満恵介, 富田大志, 井手一郎, 出口大輔, 村瀬洋

頁： 949 - 952 2014年11月

Human Tracking using a Far-Infrared Sensor Array and a Thermo-Spatial Sensitive Histogram

細野峻司, 高橋友和, 出口大輔, 井手一郎, 村瀬洋, 相澤知禎, 川出雅人

Proceedings of 2nd Workshop on User-Centred Computer Vision 2014年11月

Prediction of Driver's Pedestrian Detectability by Image Processing Adaptive to Visual Fields of View 査読有り

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

Proceedings of 2014 IEEE Conference on Intelligent Transportation Systems (ITSC2014) 頁： 1388 - 1393 2014年10月

Vision-based Vehicle Localization using a Visual Street Map with Embedded SURF Scale 査読有り

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

Proceedings of Computer Vision in Vehicle Technology with Special Session on Micro Aerial Vehicles (CVVT2014) 2014年9月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Accurate vehicle positioning is important not only for in-car navigation systems but is also a requirement for emerging autonomous driving methods. Consumer level GPS are inaccurate in a number of driving environments such as in tunnels or areas where tall buildings cause satellite shadowing. Current vision-based methods typically rely on the integration of multiple sensors or fundamental matrix calculation which can be unstable when the baseline is small.

In this paper we present a novel visual localization method which uses a visual street map and extracted SURF image features. By monitoring the difference in scale of features matched between input images and the visual street map within a Dynamic Time Warping framework, stable localization in the direction of motion is achieved without calculation of the fundamental or essential matrices.

We present the system performance in real traffic environments. By comparing localization results with a high accuracy GPS ground truth, we demonstrate that accurate vehicle positioning is achieved.

Spatial People Density Estimation from Multiple Viewpoints by Memory Based Regression 査読有り

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

Proceedings of the 22nd IAPR International Conference on Pattern Recognition (ICPR2014) 頁： 2209 - 2214 2014年8月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Crowd analysis using cameras has attracted much attention for public safety and marketing. Among techniques of the crowd analysis, we focus on spatial people density estimation which estimates the number of people for each small area in a floor region. However, spatial people density cannot be estimated accurately for an area far from the camera because of the occlusion by people in a closer area. Therefore, we propose a method using a memory based regression method with images captured from cameras from multiple viewpoints. This method is realized by looking up a table that consists of correspondences between people density maps and crowd appearances. Since the crowd appearances include situations where various occlusions occur, an estimation robust to occlusion should be realized. In an experiment, we examined the effectiveness of the proposed method.

Single Camera Vehicle Localization Using SURF Scale and Dynamic Time Warping 査読有り

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

Proceedings of 2014 the IEEE Intelligent Vehicles Symposium (IV2014) 頁： 681 - 686 2014年6月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

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 査読有り

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

Proceedings of 2014 IEEE Intelligent Vehicles Symposium (IV2014) 頁： 202 - 207 2014年6月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

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.

カメラ間の位置関係に基づく画像間距離系列を用いた車載カメラ映像データベース検索による自車位置推定査読有り

久徳遙矢, 出口大輔, 高橋友和, 目加田慶人, 井手一郎, 村瀬洋

電気学会論文誌C 134 巻 ( 5 ) 頁： 702 - 710 2014年5月

記述言語：日本語掲載種別：研究論文（学術雑誌）

We propose an accurate ego-localization method by searching a streetview database composed of single front-view in-vehicle camera images. Previously, we proposed an image distance metric reflecting the positional relation of two cameras based on epipolar geometry analysis, and used it for ego-localization. However, since the method employed a dynamic time warping strategy to avoid the effect from outliers, both input and database images needed to be image sequences. To overcome this problem, the method proposed in this paper reformulates the previous image distance metric to a novel image distance that requires only a single in-vehicle camera image as an input, which is realized by considering the sequential property of the images in the database. We conducted experiments using multiple image sequences captured under various conditions by using an in-vehicle camera mounted on the windshield of a car. The experimental results showed that the proposed method could achieve an accuracy of 89%, and we confirmed its effectiveness.

DOI： 10.1541/ieejeiss.134.702

Estimation of the Representative Story Transition in a Chronological Semantic Structure of News Topics 査読有り

Kosuke Kato, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

Proceedings of the 4th International Conference on Multimedia Retrieval (ICMR2014) 頁： 487 - 491 2014年4月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

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

Can a Human be a Sensor? 査読有り

Atsushi Shimada, Daisuke Deguchi, Kazuaki Kondo, Takuya Funatomi

Proceedings of the 20th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2014) 頁： 242 - 245 2014年2月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

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 査読有り

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

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2014 頁： 354 - 361 2014年1月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

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 査読有り

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

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2014 頁： 115 - 121 2014年1月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

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

Scene dependent classifiers for pedestrian detection

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

2013年11月

Automatic authoring of a domestic cooking video based on the description of cooking instructions 査読有り

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

Proceedings of 5th International Workshop on Multimedia for Cooking and Eating Activities (CEA'13) 頁： 21 - 26 2013年10月

Blur-invariant Traffic Sign Recognition Using Compact Local Phase Quantization 査読有り

Saleh Aly, Daisuke Deguchi, Hiroshi Murase

Proceedings of 2013 IEEE Conference on Intelligent Transportation Systems (ITSC2013) 頁： 821 - 827 2013年10月

Construction of a traffic sign detector based on voting type co-training 査読有り

Yuji Kojima, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of 2013 IEEE Conference on Intelligent Transportation Systems (ITSC2013) 頁： 1137 - 1142 2013年10月

Prediction of Pedestrian Detectability for Drivers by Image Processing and its Driver Adaptation 査読有り

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

Proceedings of the 6th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems (DSP2013) 頁： 1 - 4 2013年9月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

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.

Pedestrian Detection by Scene Dependent Classifiers with Generative Learning 査読有り

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

Proceedings of 2013 IEEE Intelligent Vehicles Symposium (IV2013) 頁： 654 - 659 2013年6月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Recently, pedestrian detection from in-vehicle camera images is becoming an crucial technology for Intelligent Transportation Systems (ITS). However, it is difficult to detect pedestrians accurately in various scenes by obtaining training samples. To tackle this problem, we propose a method to construct scene dependent classifiers to improve the accuracy of pedestrian detection. The proposed method selects an appropriate classifier based on the scene information that is a category of appearance associated with location information. To construct scene dependent classifiers, the proposed method introduces generative learning for synthesizing scene dependent training samples. Experimental results showed that the detection accuracy of the proposed method outperformed the comparative method, and we confirmed that scene dependent classifiers improved the accuracy of pedestrian detection.

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