Faculty Profiles - Deguchi Daisuke

Papers - Deguchi Daisuke

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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

Trajectory Ensemble: Multiple Persons Consensus Tracking across Non-overlapping Multiple Cameras over Randomly Dropped Camera Networks Reviewed

Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

page： 1471 - 1477 2017.7

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

Multiple person tracking over a camera network is usually performed by matching person images between adjacent cameras. It easily fails by a temporal appearance change of the persons caused by environmental illumination and observation orientation of a camera. To solve this problem, matching person images across not only adjacent cameras but also cameras multiple hops away in the camera network is effective, but such relaxation of spatiotemporal cues also cause tracking failure due to the increase of matching candidates. To avoid the failure, we introduce Random Camera Drop" to generate different camera networks which relax the spatio-temporal cues partially and randomly. We then, integrate tracking results over the networks to a consensus tracking result by a novel concept "Trajectory Ensemble", an extension of unsupervised ensemble learning for the multiple person tracking over a camera network problem. We evaluated the framework on several virtual datasets generated from a public dataset, "Shinpuhkan 2014 dataset" and confirmed that the proposed method achieves the highest tracking results among several comparative methods."

DOI： 10.1109/CVPRW.2017.190

Monocular Localization within Sparse Voxel Maps Reviewed

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

Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV2017) page： 493 - 498 2017.6

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

We introduce a method that uses a single camera to localize a vehicle within a pre-constructed map consisting of a voxel occupancy grid and road-line marker positions. Sophisticated mapping hardware is capable of creating high accuracy 3D maps of road environments, but localizing a vehicle within such maps is one of the challenges at the forefront of automated driving. A solution which is robust to dynamic environments, while using only inexpensive sensors, is a difficult problem. In addition, maps that enable precise localization consume a lot of data which is impractical for the expansive environments encountered in real-world road networks. We show how using the area of edge regions shared between rendered views of a compact voxel map and in-vehicle camera images can be coupled with non-linear optimization methods to determine the camera position and pose.

Proposal of a spectral random dots marker using local feature for posture estimation Reviewed

Norimasa Kobori, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of IEEE Virtual Reality 2017 page： 223 - 224 2017.3

Deep Manifold Embedding for 3D Object Pose Estimation Reviewed Open Access

Hiroshi Ninomiya, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Norimasa Kobori, Yusuke Nakano

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2017 Vol. 5 page： 173 - 178 2017.3

Human Wearable Attribute Recognition Using Probability-Map-Based Decomposition of Thermal Infrared Images Reviewed Open Access

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

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences Vol. E100-A ( 3 ) page： 854 - 864 2017.3

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

This paper addresses the attribute recognition problem, a
field of research that is dominated by studies in the visible spectrum. Only
a few works are available in the thermal spectrum, which is fundamentally
different from the visible one. This research performs recognition specif-
ically on wearable attributes, such as glasses and masks. Usually these
attributes are relatively small in size when compared with the human body,
on top of a large intra-class variation of the human body itself, therefore
recognizing them is not an easy task. Our method utilizes a decomposi-
tion framework based on Robust Principal Component Analysis (RPCA) to
extract the attribute information for recognition. However, because it is dif-
ficult to separate the body and the attributes without any prior knowledge,
noise is also extracted along with attributes, hampering the recognition
capability. We made use of prior knowledge; namely the location where
the attribute is likely to be present. The knowledge is referred to as the
Probability Map, incorporated as a weight in the decomposition by RPCA.
Using the Probability Map, we achieve an attribute-wise decomposition.
The results show a significant improvement with this approach compared
to the baseline, and the proposed method achieved the highest performance
in average with a 0:83 F-score.

DOI： 10.1587/transfun.E100.A.854

Open Access

Can We Detect Pedestrians using Low-resolution LIDAR? Reviewed Open Access

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

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2017 Vol. 5 page： 157 - 164 2017.2

Wheelchair-user Detection Combined with Parts-based Tracking Reviewed

Ukyo Tanikawa, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase, Ryo Kawai

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2017 Vol. 5 page： 165 - 172 2017.2

Can a Driver Assistance System Determine if a Driver is Perceiving a Pedestrian? Reviewed Open Access

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

Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP) 2017 Vol. 4 page： 611 - 616 2017.2

Single Camera Vehicle Localization Using Feature Scale Tracklets Reviewed

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

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences Vol. E100-A ( 2 ) page： 702 - 713 2017.2

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

Advances in intelligent vehicle systems have led to modern automobiles being able to aid drivers with tasks such as lane following and automatic braking. Such automated driving tasks increasingly require reliable ego-localization. Although there is a large number of sensors that can be employed for this purpose, the use of a single camera still remains one of the most appealing, but also one of the most challenging. GPS localization in urban environments may not be reliable enough for automated driving systems, and various combinations of range sensors and inertial navigation systems are often too complex and expensive for a consumer setup. Therefore accurate localization with a single camera is a desirable goal.
In this paper we propose a method for vehicle localization using images captured from a single vehicle-mounted camera and a pre-constructed database.
Image feature points are extracted, but the calculation of camera poses is not required---instead we make use of the feature points' scale.
For image feature-based localization methods, matching of many features against candidate database images is time consuming, and database sizes can become large. Therefore, here we propose a method that constructs a database with pre-matched features of known good scale stability. This limits the number of unused and incorrectly matched features, and allows recording of the database scales into ``tracklets''. These ``Feature scale tracklets'' are used for fast image match voting based on scale comparison with corresponding query image features. This process reduces the number of image-to-image matching iterations that need to be performed while improving the localization stability. We also present an analysis of the system performance using a dataset with high accuracy ground truth. We demonstrate robust vehicle positioning even in challenging lane change and real traffic situations.

DOI： 10.1587/transfun.E100.A.702

A Classification Method of Cooking Operations Based on Temporal Patterns of Gaze Transitions and Blinks Reviewed

Hiroya Inoue, Takatsugu Hirayama, Keisuke Doman, Yasutomo Kawanishi, Ichiro Ide, Daisuke Deguchi, Hiroshi Murase

IEICE Transactions on Information and Systems Vol. J100-A ( 1 ) page： 12 - 23 2017.1

Misclassification Tolerable Learning for Robust Pedestrian Orientation Classification Reviewed

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

page： 481 - 486 2016.12

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

In this paper, we propose a multiclass classifier training method which reduces fatal" misclassifications by cost-relaxation of "tolerable" misclassifications in one-against-all classifiers training, named misclassification tolerable learning. In a binary classifier in the one-against-all classifiers, we introduce a new class group "conceptually similar classes," whose class labels are similar to the positive class. In the case of pedestrian orientation classification, the conceptually similar classes are defined as neighboring orientations to the positive orientation. We consider the misclassification of the conceptually similar classes to the positive class as tolerable misclassification. By relaxing the cost of the tolerable misclassifications, our proposed classification method reduces fatal misclassifications of non-similar classes. We evaluated the cost-relaxation effectiveness on several public datasets and confirmed that the proposed method outperforms the normal SVM on all of the datasets in the soft criterion by achieving 78.63% recognition rate on PDC Dataset."

Pedestrian Detection by Scene Adaptation Based on False Positive Mining Reviewed Open Access

Yuki Suzuki, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase

Journal of the Japan Society for Precision Engineering Vol. 82 ( 12 ) page： 1085 - 1091 2016.12

Recognition of Texting-While-Walking by Joint Features based on Arm and Head Poses Reviewed

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

2016.11

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

Pedestrians texting-while-walking" increase the risk of traffic accidents, since they are often not paying attention to their surrounding environments and fails to notice approaching vehicles. Thus, the recognition of texting-while-walking from an in-vehicle camera should be helpful for safety driving assistance. In this paper, we propose a method to recognize a pedestrian texting-while-walking from in-vehicle camera images. The proposed approach focuses on the characteristic relationship between the arm and the head poses observed during a texting-while-walking behavior. In this paper, Pose-Dependent Joint HOG feature is proposed as a novel feature, which uses parts locations as prior knowledge and describes the cooccurrence of the arm and the head poses. To show the effectiveness of the proposed method, we constructed a dataset and evaluated it."

Moving camera background-subtraction for obstacle detection on railway tracks Reviewed

Hiroki Mukoujima, Daisuke Deguchi, Yasutomo Kawanishi, Ichiro Ide, Hiroshi Murase, Masato Ukai, Nozomi Nagamine, Ryuta Nakasone

Proceedings of 2016 IEEE International Conference on Image Processing (ICIP2016) page： 3967 - 3971 2016.9

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

This paper proposes a method for detecting obstacles using train frontal view videos.
In recent years, obstacles detection using in-vehicle camera is actively developed for satisfying demands from various applications.
In the field of obstacle detection, most methods employ machine learning approach, and they can detect only trained obstacles, such as pedestrian, bicycle, etc.
Therefore, they cannot detect untrained general obstacles.
To overcome this problem, this paper propose a background subtraction method using moving camera.
The proposed method first computes frame-by-frame correspondences between the present and the database train frontal view image sequences, and detects obstacles by applying image subtraction to corresponding frames.
To confirm the effectiveness of the proposed method, experiments were conducted by using several image sequences captured at the experimental railroad track.
The experimental result showed that the proposed method could detect various obstacles accurately and effectively.

DOI： 10.1109/ICIP.2016.7533104

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Papers - Deguchi Daisuke