Faculty Profiles - Deguchi Daisuke

Papers - Deguchi Daisuke

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Pedestrian Intensive Scanning for Active-scan LIDAR Reviewed

Taiki Yamamoto, Fumito Shinmura, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2019 Vol. 5 page： 313 - 320 2019.2

Language：English Publishing type：Research paper (international conference proceedings)

In recent years, LIDAR is playing an important role as a sensor for understanding environments of a vehicle’s surroundings. Active-scan LIDAR is being actively developed as a LIDAR that can control the laser irradiation direction arbitrary and rapidly. In comparison with conventional uniform-scan LIDAR (e.g. Velodyne HDL-64e), Active-scan LIDAR enables us to densely scan even distant pedestrians. In addition, if appropriately controlled, this sensor has the potential to reduce unnecessary laser irradiations towards non-target objects. Although there are some preliminary studies on pedestrian scanning strategy for Active-scan LIDARs, in the best of our knowledge, an efficient method has not been realized yet. Therefore, this paper proposes a novel pedestrian scanning method based on orientation aware pedestrian likelihood estimation using the orientation-wise pedestrian’s shape models with local distribution of measured points. To evaluate the effectiveness of the proposed method, we conducted experiments by simulating Active-scan LIDAR using point-clouds from the KITTI dataset. Experimental results showed that the proposed method outperforms conventional methods.

Voting-based Hand-Waving Gesture Spotting from a Low-Resolution Far-Infrared Image Sequence Reviewed

Yasutomo Kawanishi, Chisato Toriyama, Tomokazu Takahashi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Tomoyoshi Aizawa, Masato Kawade

Proceedings of 2018 IEEE International Conference on Visual Communications and Image Processing (VCIP2018) 2018.12

Language：English Publishing type：Research paper (international conference proceedings)

We propose a temporal spotting method of a hand gesture from a low-resolution far-infrared image sequence captured by a far-infrared sensor array. The sensor array captures the spatial distribution of far-infrared intensity as a thermal image by detecting far-infrared waves emitted from heat sources. It is difficult to spot a hand gesture from a sequence of thermal images captured by the sensor due to its low-resolution, heavy noise, and varying duration of the gesture. Therefore, we introduce a voting-based approach to spot the gesture with template matching-based gesture recognition. We confirm the effectiveness of the proposed temporal spotting method in several settings.

DOI： 10.1109/VCIP.2018.8698650

Gaze-inspired Learning for Estimating the Attractiveness of a Food Photo Reviewed

Akinori Sato, Takatsugu Hirayama, Keisuke Doman, Yasutomo Kawanishi, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

Proceedings of 20th IEEE Int. Symposium on Multimedia (ISM2018) page： 36 - 43 2018.12

Language：English Publishing type：Research paper (international conference proceedings)

The number of food photos posted to the Web has been increasing. Most of the users prefer to post delicious-looking food photos. They, however, do not always look delicious. A previous work proposed a method for estimating the attractiveness of food photos, that is, the degree of how much a food photo looks delicious, as an assistive technology for taking a delicious-looking food photo. This method extracted image features from the entire food photo to evaluate the impression. In our work, we conduct a preference experiment where subjects are asked to compare a pair of food photos and measure their gaze. The proposed method extracts image features from local regions selected based on the gaze information and estimates the attractiveness of a food photo by learning regression parameters. Experimental results showed the effectiveness of extracting image features from outside the gaze regions rather than inside them.

DOI： 10.1109/ISM.2018.00015

Action Recognition Using a Far-Infrared Sensor Array by Convolutional RNN Reviewed

Takayuki Kawashima, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Tomoyoshi Aizawa, Masato Kawade

Journal of the Japan Society for Precision Engineering Vol. 84 ( 12 ) page： 1025 - 1032 2018.12

Pedestrian Detection from a Sparse LIDAR Point-Cloud Reviewed

Yoshiki Tatebe, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase

Journal of the Japan Society for Precision Engineering Vol. 84 ( 12 ) page： 1017 - 1024 2018.12

Localizing the Gaze Target of a Crowd of People Reviewed

Yuki Kodama, Yasutomo Kawanishi, Takatsugu Hirayama, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Hidehisa Nagano, Kunio Kashino

2018.12

Language：English Publishing type：Research paper (international conference proceedings)

What target is focused on by many people?
Analysis of the target is a crucial task, especially in a cinema, a stadium, and so on.
However, it is very difficult to estimate the gaze of each person in a crowd accurately and simultaneously with existing image-based eye tracking methods, since the image resolution of each person becomes low when we capture the whole crowd with a distant camera.
Therefore, we introduce a new approach for localizing the gaze target focused on by a crowd of people.
The proposed framework aggregates the individually estimated results of each person's gaze.
It enables us to localize the target being focused on by them even though each person's gaze localization from a low-resolution image is inaccurate.
We analyze the effects of an aggregation method on the localization accuracy using images capturing a crowd of people in a tennis stadium under the assumption that all of the people are focusing on the same target, and also investigate the effect of the number of people involved in the aggregation on the localization accuracy.
As a result, the proposed method showed the ability to improve the localization accuracy as it is applied to a larger crowd of people.

Analyzing Headlight Flicker Patterns for Improving the Pedestrian Detectability from a Driver Reviewed

Takashi Maeda, Takatsugu Hirayama, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of 2018 IEEE 21st International Conference on Intelligent Transportation Systems (ITSC2018) page： 3113 - 3118 2018.11

Language：English Publishing type：Research paper (international conference proceedings)

In this paper, we analyze headlight flicker patterns which improve the pedestrian detectability from a driver. Recently, headlights are becoming capable of selectively projecting light on a pedestrian in addition to the normal forward projection. However, it is still not clear how the light should be
projected to effectively improve the visibility of the pedestrian. We actually analyze nine flicker patterns by controlling duty ratios and durations of lighting time, and conduct experiments in field and laboratory settings. As a result, we reveal that a specific fundamental frequency is effective for improving the pedestrian detectability from a driver. We also conclude that the difference between the two settings are not significant.

Estimating the Scene-wise Reliability of LiDAR Pedestrian Detectors Reviewed

Haruya Kyutoku, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Kazuki Kato, Hiroshi Murase

Proceedings of 2018 IEEE 21th International Conference on Intelligent Transportation Systems (ITSC2018) page： 3511 - 3516 2018.11

Language：English Publishing type：Research paper (international conference proceedings)

Nowadays, development of driving support systems and autonomous driving systems have become active.
Pedestrian detection from in-vehicle sensors is one of the most important technologies for these systems.
However, outputs of pedestrian detectors can not be fully trusted in real environments.
Therefore, we propose an estimation system of pedestrian detector's reliabilities for a given scene.
This paper proposes a scene-wise reliability calculation method for LiDAR-based detectors, and a construction method for their estimators.
Here, the problem is how we can define the reliability.
The proposed method defines the reliability considering oversights as the strictest threshold without oversights.
Meanwhile, it defines the reliability considering false detections as the loosest threshold without false detections.
Experimental results showed that the proposed method could properly represent the reliability of a given scene, and estimate their reliability.

Attribute-aware Semantic Segmentation of Road Scenes for Understanding Pedestrian Orientations Reviewed

Mahmud Dwi Sulistiyo, Yasutomo Kawanishi, Daisuke Deguchi, Takatsugu Hirayama, Ichiro Ide, Jiang-Yu Zheng, Hiroshi Murase

Proceedings of 2018 IEEE 21th International Conference on Intelligent Transportation Systems (ITSC2018) page： 2698 - 2703 2018.11

Language：English Publishing type：Research paper (international conference proceedings)

Semantic segmentation is an interesting task for many deep learning researchers for scene understanding. However, recognizing details about objects’ attributes can be more informative and also helpful for a better scene understanding in intelligent vehicle use cases. This paper introduces a method for simultaneous semantic segmentation and pedestrian attributes recognition. A modified dataset built on top of the Cityscapes dataset is created by adding attribute classes corresponding to pedestrian orientation attributes. The proposed method extends the SegNet model and is trained by using both the original and the attribute-enriched datasets. Based on an experiment, the proposed attribute-aware semantic segmentation approach shows the ability to slightly improve the performance on the Cityscapes dataset, which is capable of expanding its classes in this case through additional data training.

Estimation of Driver's Insight for Safe Passing based on Pedestrian Attributes Reviewed

Fumito Shinmura, Yasutomo Kawanishi, Daisuke Deguchi, Takatsugu Hirayama, Ichiro Ide, Hiroshi Murase, Hironobu Fujiyoshi

Proceedings of 2018 IEEE 21th International Conference on Intelligent Transportation Systems (ITSC2018) page： 1041 - 1046 2018.11

Language：English Publishing type：Research paper (international conference proceedings)

In order to reduce traffic accidents between a vehicle and a pedestrian, recognition of a pedestrian who has a possibility of collision with a vehicle should be helpful. However, since a pedestrian may suddenly change his/her direction and cross the road, it is difficult to predict his/her behavior directly. Here, we focus on the fact that experienced drivers usually pass by a pedestrian while preparing to step on the brake at any moment when they feel danger. If driver assistant systems can estimate such experienced driver’s decisions, they could early detect the pedestrian in danger of collision. Therefore, we classify the driver’s decisions into three types by referring to the accelerator operation of drivers, and propose a method to estimate the type of the driver’s decision. The drivers are considered to decide their actions focusing on various behaviors and states of a pedestrian, namely pedestrian’s attributes. Since the driver’s decisions change along the timeline, the use of a temporal context is considered to be effective. Thus, in this paper, we propose an estimation method using a recurrent neural network architecture with the pedestrian’s attributes as input. We constructed a dataset collected by experienced drivers in control of the vehicle and evaluated the performance, and then confirmed the effectiveness of the use of pedestrian’s attributes.

Image Synthesis of Eye Areas for Perceptually Establishing Eye-contacts between Video Conference Participants Reviewed

Takuya Inoue, Takatsugu Hirayama, Tomokazu Takahashi, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Takayuki Kurozumi, Kunio Kashino

IEEJ Transactions on Electronics, Information and Systems Vol. 138 ( 11 ) page： 1399 - 1409 2018.11

Epipolar geometry-based ego-localization using an in-vehicle monocular camera

Haruya Kyutoku, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

2018.10

Language：English Publishing type：Research paper (international conference proceedings)

Nowadays, development of driving support systems and autonomous driving systems have become active.
Vehicle ego-localization using in-vehicle sensors is one of the most important technologies for these systems.
Accordingly, various attempts to localize own vehicle from in-vehicle sensors have been made.
In general, the estimation accuracy of the traveling direction is lower than in the lateral direction.
Therefore, we propose a highly accurate method for ego-localization of the traveling direction based on epipolar geometry using an in-vehicle monocular camera.
The proposed method makes correspondences between in-vehicle camera images and database images with location information, and calculates the location using locations annotated to the corresponding database images.
However, there are many gaps due to the difference of speed and trajectory of vehicles even if the images are obtained along the same road.
To overcome this problem, the distance between the input image and the database image is calculated by the distance metric based on the epipolar geometry and the local feature method.
An experiment was conducted using actual images with correct locations, and we confirmed the effectiveness of the proposed method from its results.

Vehicle counting via car parts detection from an in-vehicle camera image

Fumito Shinmura, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

2018.10

Language：English Publishing type：Research paper (international conference proceedings)

This paper proposes a method to count vehicles from an in-vehicle camera image by regression based on car parts detection. In the case of an in-vehicle camera image, since vehicles are frequently occluded by other vehicles in traffic congestion, it is difficult to accurately count vehicles. Therefore, we propose a method to count vehicles by regression based on the number of visible car parts. For this, we make an estimator by learning the relation between the number of visible car parts and that of vehicles by Support Vector Regression. We evaluated our method using in-vehicle camera images recorded in an actual environment, where the proposed method performed better than counting detected vehicles.

People Tracking across Non-overlapping Camera Views via Camera Dropout and Trajectory Ensemble Reviewed

Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEICE Transactions on Information and Systems Vol. J101-D ( 8 ) page： 1079 - 1088 2018.8

Visualization of Real World Activity on Group Work

Daisuke Deguchi, Kazuaki Kondo, Atsushi Shimada

Proceedings of 20th International Conference on HCI International 2018 (HCII2018) Vol. LNCS 10902 page： 23 - 37 2018.7

Authorship：Lead author Language：English Publishing type：Research paper (international conference proceedings)

Group work is widely introduced and practiced as a method to achieve the learning goal efficiently by collaborating group members. However, since most types of group works are carried out in the real environment, it is very difficult to perform formative assessment and real time evaluation without students' feedbacks. Therefore, there is a strong demand to develop a method that supports evaluation of group work. To support evaluation of group work, this paper proposes a method to visualize the real world activity during group work by using first person view cameras and wearable sensors. Here, the proposed method visualizes three scores: (1) individual attention, (2) hand visibility, (3) individual activity. To evaluate the performance and analyze the relationships between scores, we conducted experiments of Marshmallow challenge" that is a collaborative work to construct a tower using marshmallow and spaghetti within a limit of time. Through the experiments, we confirmed that the proposed method has potential to become a evaluation tool for visualizing the activity of the group work."

DOI： 10.1007/978-3-319-91131-1_2

Describing Gaits by Onomatopoeias with Sound Symbolism Reviewed

Hirotaka Kato, Takatsugu Hirayama, Keisuke Doman, Ichiro Ide, Yasutomo Kawanishi, Daisuke Deguchi, Hiroshi Murase

Transactions of the Japanese Society for Artificial Intelligence Vol. 33 ( 4 ) page： 1 - 9 2018.7

Pedestrian Detectability Estimation Considering Visual Adaptation to Drastic Illumination Change Reviewed

Yuki Imaeda, Takatsugu Hirayama, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEICE Transactions on Information and Systems Vol. E101-D ( 5 ) page： 1457 - 1462 2018.5

Development of 'KamiRepo' System with Automatic Student Identification to Handle Handwritten Assignments on LMS Reviewed

Shunya Seiya, Ryuya Ito, Kosuke Okamoto, Ukyo Tanikawa, Shigeki Ohira, Daisuke Deguchi, Tomoki Toda

Proceedings of 2018 the IEEE Global Engineering Education Conference (EDUCON2018) page： 841 - 848 2018.4

Language：English Publishing type：Research paper (international conference proceedings)

A Learning Management System (LMS) has become a fundamental tool for higher education, and a framework to leverage digital education data in the LMS has attracted attention. On the other hand, there is strong demand to deal with various education data provided not only from electronic media but also non-electronic media, such as a handwritten assignment. To solve this problem, this paper describes the development of 'KamiRepo' system to make it possible to automatically upload handwritten assignments to the LMS. In this system, optical character recognition (OCR) is performed to identify scanned handwritten assignments of individual students and read their scores. Then, their scanned files automatically separated from the entire file of the scanned handwritten assignments are returned to the individual students through LMS together with their corresponding scores. Compared with a conventional system using the dedicated multifunction printer, our developed system is capable of 1) using general-purpose scanners, 2) using a user interface on Web browser, and 3) achieving accurate student identification. We have launched this system in our university in April 2017 and have evaluated its effectiveness. The experimental results using real data collected for 6 months showed that our system achieved 99.7% of success rate in the automatic upload process.

A Preliminary Study on Optimizing Person Re-identification using Stable Marriage Algorithm Reviewed

Nik Mohd Zarifie Hashim, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

page： 6 2018.2

Language：English Publishing type：Research paper (international conference proceedings)

Person re-identification gains an increasing interest
in the surveillance image processing field due to its’ importance
for security. Most approaches to solve the person re-identification
problem match persons one-by-one. However, redundant match-
ing where one of the person is selected for the matching pair
several times often occurs. It also degrades the overall image
matching performance. To overcome the issue, in this paper,
we propose a method which solves the person re-identification
problem for multiple persons simultaneously. Instead of one-
by-one matching, we consider person re-identification as an
instance of the Stable Marriage Problem (SMP). The result of an
experiment showed that the proposed method outperforms some
of the existing state-of-the-art methods applied to the VIPeR
dataset.

Efficient Pedestrian Scanning by Active Scan LIDAR Reviewed

Taiki Yamamoto, Fumito Shinmura, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase

Proceedings of International Workshop on Advanced Image Technology (IWAIT) 2018 page： 1 - 4 2018.1

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Papers - Deguchi Daisuke