Faculty Profiles - Deguchi Daisuke

Papers - Deguchi Daisuke

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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."

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

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

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.

Can We Detect Pedestrians using Low-resolution LIDAR? Reviewed

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

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

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

Subjective Sensing of Real World Activity on Group Study Reviewed

Daisuke Deguchi, Kazuaki Kondo, Atsushi Shimada

Proceedings of the 18th International Conference on Collaboration Technologies (CollabTech2016) page： 5 - 8 2016.9

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

Collaborative learning is efficient teaching/learning method, and it is widely introduced and practiced in various situations. However, it has a difficulty to perform formative assessment and real time evaluation without students' feedbacks. Therefore, demand for technologies to support formative assessment in collaborative learning is increasing. To tackle this problem, we have started the research project for automatic sensing and visualization of real world activities in collaborative learning. In this paper, we will report details about preliminary group work experiments and its results with visualization tool.

Proposal of an Encoded Marker for Working Robots Reviewed

Norimasa Kobori, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEEJ Transactions on Electronics, Information and Systems Vol. 136 ( 9 ) page： 1367 - 1375 2016.9

Parts Selective DPM for detection of pedestrians possessing an umbrella Reviewed

Yuto Shimbo, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

Proceedings of 2016 IEEE Intelligent Vehicles Symposium (IV2016) page： 1053 - 1058 2016.6

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

In recent years, pedestrian detection from an invehicle camera has been attracting attention.However, in the case of a raining situation, the detection accuracy decreases because the head of a pedestrian tends to be occluded by an umbrella. In oder to handle such cases, in this paper, as a variation of the Deformable Part Model (DPM) which is widely used in the field of object recognition, we propose Parts Selective DPM (PS-DPM)" which selectively chooses the original part filters and additional part filters trained independently. In the detection of pedestrians possessing an umbrella, the selection of head and umbrella parts will make pedestrian detection more robust to the occlusion. We conducted experiments to evaluate the performance of the proposed method. As a result, pedestrian detection with the proposed PS-DPM achieved high detection accuracy in rainy weather, compared with the detection by the conventional DPM. Moreover, we confirmed that it did not decrease the pedestrian detection accuracy in fine weather."

Using Super-Pixels and Human Probability Map for Automatic Human Subject Segmentation Reviewed

Esmaeil Pourjam, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences Vol. E99-A ( 5 ) page： 943 - 953 2016.5

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

Human body segmentation has many applications in a wide
variety of image processing tasks, from intelligent vehicles to entertainment.
A substantial amount of research has been done in the field of segmentation
and it is still one of the active research areas, resulting in introduction of
many innovative methods in literature. Still, until today, a method that can
overcome the human segmentation problems and adapt itself to different
kinds of situations, has not been introduced. Many of methods today try to
use the graph-cut framework to solve the segmentation problem. Although
powerful, these methods rely on a distance penalty term (intensity difference
or RGB color distance). This term does not always lead to a good separation
between two regions. For example, if two regions are close in color, even
if they belong to two different objects, they will be grouped together, which
is not acceptable. Also, if one object has multiple parts with different
colors, e.g. humans wear various clothes with different colors and patterns,
each part will be segmented separately. Although this can be overcome
by multiple inputs from user, the inherent problem would not be solved.
In this paper, we have considered solving the problem by making use of a
human probability map, super-pixels and Grab-cut framework. Using this
map relives us from the need for matching the model to the actual body,
thus helps to improve the segmentation accuracy. As a result, not only the
accuracy has improved, it also became comparable to the state-of-the-art
interactive methods.

DOI： 10.1587/transfun.E99.A.943

A study on estimating the attractiveness of food photography Reviewed

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

Proceedings of 2016 IEEE Second International Conference on Multimedia Big Data page： 444 - 449 2016.4

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

This paper proposes a method for estimating the attractiveness of food photos in order to assist a user to shoot them attractively.
The proposed method extracts both color and shape features from input food images, and then integrates them according to a regression scheme.
By this way, the proposed method estimates the attractiveness of an unknown food photo.
We also created a food image dataset taken from various 3D-angles for each food category, and set target values of their attractiveness through subjective experiments.
Then, we evaluated the performance of the proposed method in two different ways of constructing the attractiveness estimator: One that constructs it for each food category, and the other that constructs a common attractiveness estimator for all food categories.
Experimental results showed the effectiveness of the proposed method in addition to the necessity for adaptively selecting the estimator depending on the appearance of foods for further performance improvement.

Reconstructing Face Image from the Thermal Infrared Spectrum to the Visible Spetrum Reviewed

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

Sensors, special issue on "Infrared and THz Sensing and Imaging" Vol. 16 ( 4 ) page： 1 - 16 2016.4

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

During the night or in poorly lit areas, thermal cameras are a better choice instead of normal cameras for security surveillance because they do not rely on illumination. A thermal camera is able to detect a person within its view, but identification from only thermal information is not an easy task. The purpose of this paper is to reconstruct the face image of a person from the thermal spectrum to the visible spectrum. After the reconstruction, further image processing can be employed, including identification/recognition. Concretely, we propose a two-step thermal-to-visible-spectrum reconstruction method based on Canonical Correlation Analysis (CCA). The reconstruction is done by utilizing the relationship between images in both thermal infrared and visible spectra obtained by CCA. The whole image is processed in the first step while the second step processes patches in an image. Results show that the proposed method gives satisfying results with the two-step approach and outperforms comparative methods in both quality and recognition evaluations.

DOI： 10.3390/s16040568

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Papers - Deguchi Daisuke