Faculty Profiles - Deguchi Daisuke

Papers - Deguchi Daisuke

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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

Pedestrian Detection from Sparse Point-Cloud using 3DCNN Reviewed

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

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

Automatic selection of Web contents towards automatic authoring of a video biography Reviewed

Ichiro Ide, Yasutomo Kawanishi, Kyoka Kunishiro, Frank Nack, Daisuke Deguchi, Hiroshi Murase

Proceedings of 19th IEEE Int. Symposium on Multimedia (ISM2017) page： 304 - 307 2017.12

Summarization of news videos considering the consistency of auditory and visual contents Reviewed

Ichiro Ide, Ye Zhang, Ryunosuke Tanishige, Keisuke Doman, Yasutomo Kawanishi, Daisuke Deguchi, Hiroshi Murase

Proceedings of 19th IEEE Int. Symposium on Multimedia (ISM2017) page： 193 - 199 2017.12

Toward Describing Human Gaits by Onomatopoeias Reviewed

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

Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV2017) Workshops page： 1573 - 1580 2017.10

Driver's Decision Analysis in Terms of Pedestrian Attributes -A Case Study in Passing by a Pedestrian-

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

Proceedings of Workshop on Human Factors in Intelligent Vehicles page： 32 - 36 2017.10

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

In this paper, we report a case study on driver's decision in terms of pedestrian attributes. Among various traffic situations, the situation that a vehicle passes by a pedestrian is one of the major situations. To build a safety driving system that supports a non-experienced driver in such a situation, we analyzed how experienced drivers decide to handle the vehicle in such a situation. Since pedestrian’s behavior can be considered as a key factor for the decision, and also the behavior is different depending on their attributes," such as walking or stopping, noticing the vehicle or not, using a smartphone, etc., we analyzed what pedestrian's attributes affect the driver's decisions. For the analysis, we first built a large-scale dataset of driving data. Using the dataset, we clarified what attributes are dominant for the driver's decision."

Detection of Similar Geo-Regions based on Visual Concepts in Social Photos Reviewed

Hiroki Takimoto, Magali Philippe, Yasutomo Kawanishi, Ichiro Ide, Takatsugu Hirayama, Keisuke Doman, Daisuke Deguchi, Hiroshi Murase

2017.9

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

Travel destination recommendation is useful to support travel.Considering the recommendation of regions within the destination area to visit, it could be difficult for the users to explicitly indicate their preference.Therefore, we considered that it would be more intuitive to recommend regions in the destination area that are similar to a region already well-known to the user.Thus, in this paper, we propose a method for the detection of similar geo-regions based on Visual Concepts in social photos.We report experimental results and analyses by applying the proposed method to the YFCC100M dataset.

Proposal of an encoded marker for working robots: An encoded marker easy to detect in various positions and under blur Reviewed

Norimasa Kobori, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Electronics and Communications in Japan Vol. 100 ( 10 ) page： 59 - 69 2017.9

Action Recognition from Extremely Low-Resolution Thermal Image Sequence Reviewed

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

Proceedings of the 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS2017) 2017.9

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

This paper proposes a Deep Learning-based action recognition method from an extremely low-resolution thermal image sequence. The method recognizes daily actions by humans (e.g. walking, sitting down, standing up, etc.) and abnormal actions (e.g. falling down) without privacy concerns. While privacy concerns can be ignored, it is difficult to compute feature points and to obtain a clear edge of the human body from an extremely low-resolution thermal image. To address these problems, this paper proposes a Deep Learning-based action recognition method that combines convolution layers and an LSTM layer for learning spatio-temporal representation, whose inputs are the thermal images and their frame differences cropped by the gravity center of human regions. The effectiveness of the proposed method was confirmed through experiments.

DOI： 10.1109/AVSS.2017.8078497

Estimation of the attractiveness of food photography focusing on main ingredients Reviewed

Kazuma Takahashi, Keisuke Doman, Yasutomo Kawanishi, Takatsugu Hirayama, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

Proceedings of 9th Workshop on Cooking and Eating Activities (CEA2017) in conjunction with IJCAI2017 page： 1 - 6 2017.8

Regression of Feature Scale Tracklets for Decimeter Visual Localization Reviewed

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

Image and Vision Computing Vol. 68 page： 53 - 63 2017.8

Language：English Publishing type：Research paper (scientific journal)

Localization along a route is an everyday necessity for in-vehicle navigation systems, and a vital task for automated driving technologies. Visual ego-localization promises reliable accuracy even in challenging urban environments where Global Positioning Systems (GPSs) can fail. Using cameras for localization against a pre-constructed database requires either the creation of a dense three-dimensional feature point map and pose estimation of a query camera relative to this map, or image matching along a database route to determine the capture position of the query camera based on the most similar database image. While the latter method is potentially less computationally intensive and can provide a more compact database, localization accuracy is limited by the discrete positioning information at database frame capture locations. In this paper we propose an image matching method that makes use of image features which are pre-matched during database construction, allowing linear regression coefficients for the relationship between capture position and feature scale to be calculated. The capture position of matched query features can then be estimated to sub-database spacing resolution. By incorporating the visual localization system into a Bayes estimator, we demonstrate an average monocular vision localization accuracy of 0.33 m in tests on actual vehicle image streams.

DOI： 10.1016/j.imavis.2017.07.004

Headgear Recognition by Decomposing Human Images in the Thermal Infrared Spectrum Reviewed

Brahmastro Kresnaraman, Yasutomo Kawanishi, Daisuke Deguchi, Tomokazu Takahashi, Yoshito Mekada, Ichiro Ide, Hiroshi Murase

Proceedings of the 15th International Conference on Quality in Research (QiR2017) page： 164 - 168 2017.7

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

Surveillance systems play a critical role in security and surveillance. A surveillance system with cameras that work in the visible spectrum is sufficient for most cases. However, problems may arise during the night, or in areas with less than ideal illumination conditions. Cameras with thermal infrared technology can be a better option in these situations since they do not rely on illumination to observe the environment. Furthermore, in our daily lives, it is common for humans to wear headgears such as glasses, masks, and hats. In surveillance, such headgears can be a hindrance to the identification of a person, and hence pose a certain degree of risk. This is not ideal in areas where the identity of a person is important, for example, in a bank. Therefore, in this paper we propose a headgear recognition method using an innovative decomposition approach on thermal infrared images. The decomposition method is based on Robust Principal Component Analysis, a modification of the popular Principal Component Analysis. The proposed method performs decomposition on a human image and isolates headgears in the image for recognition purposes. Experiments were conducted to evaluate the capability of the proposed method. The results show a positive outcome when compared with other methods.

DOI： 10.1109/QIR.2017.8168475

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