Details of a Researcher - Aoki, Yoshimitsu

Papers - Aoki, Yoshimitsu

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追跡軌跡の再同定を用いたオンライン複数物体追跡

加藤直樹，田靡雅基，古山純子，里雄二，青木義満

画像電子学会論文誌 (画像電子学会) 47 ( 4 ) 391 - 400 2018.11

Research paper (scientific journal), Joint Work, Accepted

Suzuki T., Akizuki S., Kato N., Aoki Y.

Proceedings - International Conference on Image Processing, ICIP (Proceedings - International Conference on Image Processing, ICIP) 3249 - 3253 2018.08

ISSN 9781479970612

© 2018 IEEE. In this paper, we propose a novel segmentation algorithm based on convolutional neural networks (CNNs) on superpix-else CNNs are powerful methods for several computer vision tasks, but spatial information disappears through the pooling process. Moreover, since pooling compresses different types of pixels into a single value, pooling sometimes negatively affects the results of inference in segmentation task. We use superpixel pooling instead of general pooling to resolve this problem. However, general CNNs can't use superpixel images in which the adjacency relationships between pixels are broken. Therefore, we define CNNs and Dilated Convolution on superpixels. Finally, we show the effectiveness of proposed method on an HKU-IS dataset.

Access to Document (DOI)

Segmentationのためのスーパーピクセル上でのGraph Convolutional Neural Networksの提案

鈴木哲平，青木義満

電子情報通信学会和文論文誌D (電子情報通信学会) J101-D ( 8 ) 1120 - 1129 2018.08

Research paper (scientific journal), Joint Work, Accepted

Estimation of Scenes Contributing to Score in Tennis Video Using Attention

Kurose R., Hayashi M., Aoki Y.

Proceedings - 14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018 (Proceedings - 14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018) 672 - 675 2018.07

ISSN 9781538693858

The use of image processing technology for sports is increasing. By analyzing athletes and teams based on video analysis, scientific and objective analysis can be conducted separately from subjective analysis of experts, and individuals or teams can be evaluated with the same index. As can be seen from such a trend, recently, technological progress has made it possible to detect and analyze what can be visually confirmed in the sports video. It is certain that these technologies are helping coaches in the analysis of movement. However, these cannot play the role of coaches. The role is to judge what is important from what can be visually confirmed. There are also empirical and qualitative parts in coach's judgment, and no reproducibility of understanding the important parts unless it is a specialist. Based on this background, we thought that it would be useful to extract more important scenes during the game using quantitative information. Such technology can be applied to various sports, but this paper, we focused on tennis. In this thesis, the aim is to estimate which play was largely contributed to the tennis game result (score, failure). In addition, we don't use supervised information that can be obtained from an empirical point of view. This is to eliminate dataset dependency caused by using data created from qualitative information. Specifically, based on quantitative information such as athletes' movement and score result, we attempted to estimate the attention from unsupervised method.

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Single Image, Context Aware Action Estimation in Sports

Lanius C., Kobayashi D., Ouchi K., Aoki Y.

ISSN 9781538693858

Sports video analysis enables professional teams to prepare and practice more efficiently than ever before. It allows the team to work on their weak points and analyze upcoming opponents' strategies. As segmentation is a well researched topic, we assume a sports video where each frame is segmented into actors. We show that the location of the actors is correlated to their action. We develop a model to predict each actor's action in a context consistent and location aware manner. The set of context actors is an unordered set, thus we propose a permutation invariant structure for our classifier. We evaluate a location and a context aware network for single image sports action recognition on two datasets. One is an existing dataset of volleyball games. We present a new dataset for actor recognition in rugby. We compare the results to using only the image of the actor.

Access to Document (DOI)

Swimmer's stroke estimation using CNN and MultiLSTM

Kohei Hakozaki, Noki Kato, Masamoto Tanabiki, Junko Furuyama, Yuji Sato, Yoshimitsu Aoki

Journal of Signal Processing (RISP) 22 ( 4 ) 219 - 222 2018.07

Research paper (scientific journal), Joint Work, Accepted

Player pose analysis in tennis video based on pose estimation

Kurose R., Hayashi M., Ishii T., Aoki Y.

2018 International Workshop on Advanced Image Technology, IWAIT 2018 (2018 International Workshop on Advanced Image Technology, IWAIT 2018) 1 - 4 2018.05

ISSN 9781538626153

© 2018 IEEE. The demand for sports video analysis is expanding. Sports video analysis is used to analyze their own play and play of opponent players, and visualize movement and ability of players. Scientific and objective analysis become possible by incorporating video analysis. It is desired to improve the competition level by feeding back the analysis results to the players. Therefore, in this research, in the case of feeding back the analysis result to the athlete, research purpose is to realize a method which can understand and evaluate the details of the form hitting the ball in detail. First, joint position coordinates are estimated from input RGB tennis images by a posture estimation method. The joint position coordinates in each frame at the time of shot are classified using unsupervised method and represented by BoW. The feature vector is designed by combining this with the shot position. The probability of shot success is predicted using this feature vector. By visualizing BoW of the shot with the high probability of success and the high failure probability, it is possible to extract and compare poses that are likely to appear in each case without giving correct labels.

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Pose alignment for different objects using affordance cues

Akizuki S., Aoki Y.

2018 International Workshop on Advanced Image Technology, IWAIT 2018 (2018 International Workshop on Advanced Image Technology, IWAIT 2018) 1 - 3 2018.05

ISSN 9781538626153

© 2018 IEEE. In this paper, we propose a method to estimate 6D pose (3D position and pose) of the everyday objects, especially tools, even if there are not the same 3D model of the target object. We assume that the tools that belong to same category have common spatial relation in part-affordances, the role of each part. The proposed method uses this assumption for pose estimation. By minimizing two types of consistencies, the spatial relationship of part-affordances and the shapes, we have confirmed that the proposed method achieved higher performance compared with traditional pose estimation method.

Access to Document (DOI)

Temporal and Fine-grained Pedestrian Action Recognition on Driving Recorder Database

Hirokatsu Kataoka, Yutaka Satoh, Yoshimitsu Aoki, Shoko Oikawa, Yasuhiro Matsui

Sensors (MDPI) 18 ( 2 ) 2018.02

Research paper (scientific journal), Joint Work, Accepted, ISSN 14248220

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. The paper presents an emerging issue of fine-grained pedestrian action recognition that induces an advanced pre-crush safety to estimate a pedestrian intention in advance. The fine-grained pedestrian actions include visually slight differences (e.g., walking straight and crossing), which are difficult to distinguish from each other. It is believed that the fine-grained action recognition induces a pedestrian intention estimation for a helpful advanced driver-assistance systems (ADAS). The following difficulties have been studied to achieve a fine-grained and accurate pedestrian action recognition: (i) In order to analyze the fine-grained motion of a pedestrian appearance in the vehicle-mounted drive recorder, a method to describe subtle change of motion characteristics occurring in a short time is necessary; (ii) even when the background moves greatly due to the driving of the vehicle, it is necessary to detect changes in subtle motion of the pedestrian; (iii) the collection of large-scale fine-grained actions is very difficult, and therefore a relatively small database should be focused. We find out how to learn an effective recognition model with only a small-scale database. Here, we have thoroughly evaluated several types of configurations to explore an effective approach in fine-grained pedestrian action recognition without a large-scale database. Moreover, two different datasets have been collected in order to raise the issue. Finally, our proposal attained 91.01% on National Traffic Science and Environment Laboratory database (NTSEL) and 53.23% on the near-miss driving recorder database (NDRDB). The paper has improved +8.28% and +6.53% from baseline two-stream fusion convnets.

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Video-Based Person Re-identification by 3D Convolutional Neural Networks and Improved Parameter Learning

Kato N., Hakozaki K., Tanabiki M., Furuyama J., Sato Y., Aoki Y.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)) 10882 LNCS 153 - 163 2018

ISSN 9783319929996

© 2018, Springer International Publishing AG, part of Springer Nature. In this paper we propose a novel approach for video-based person re-identification that exploits convolutional neural networks to learn the similarity of persons observed from video camera. We take 3-dimensional convolutional neural networks (3D CNN) to extract fine-grained spatiotemporal features from the video sequence of a person. Unlike recurrent neural networks, 3D CNN preserves the spatial patterns of the input, which works well on re-identification problem. The network maps each video sequence of a person to a Euclidean space where distances between feature embeddings directly correspond to measures of person similarity. By our improved parameter learning method called entire triplet loss, all possible triplets in the mini-batch are taken into account to update network parameters. This parameter updating method significantly improves training, enabling the embeddings to be more discriminative. Experimental results show that our model achieves new state of the art identification rate on iLIDS-VID dataset and PRID-2011 dataset with 82.0%, 83.3% at rank 1, respectively.

Access to Document (DOI)

Simultaneous execution of color adjustment and image completion by generative adversarial network

Akimoto N., Hayashi M., Akizuki S., Aoki Y.

Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering (Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering) 84 ( 12 ) 1033 - 1040 2018

ISSN 09120289

In this paper, we address the problem of performing natural paste synthesis by color adjustment and image completion, in order to solve the completion problem that can specify an object appearing in a completion area. We propose a synthesis network that can extract the context features of the input image and reconstruct an image with the feature, making the inserted object appear in the completion region. In addition, we propose a ingenious method to make input images and learning method using Generative Adversarial Network (GAN) that do not require collection of high cost learning data. We show that color adjustment and image completion based on context features are executed at the same time, and natural pasting synthesis can be performed by using these proposal methods.

Access to Document (DOI)

Online multi-object tracking with tracklet re-identification

Kato N., Tanabiki M., Furuyama J., Sato Y., Aoki Y.

Journal of the Institute of Image Electronics Engineers of Japan (Journal of the Institute of Image Electronics Engineers of Japan) 47 ( 4 ) 391 - 400 2018

ISSN 02859831

One of the major factors that makes the task of tracking multiple objects difficult is frequent occlusions in congestion situations. Many existing methods in multi-object tracking by online processing have adopted a tracking-by-detection approach to perform object detection in every frame of a video and associate the object bounding box obtained by it temporally. However, with the existing method, it was not possible to track objects that could not be acquired by detectors due to occlusion. Therefore, we propose a method to shift the state of the lost target to tracked state once again by tracklet re-identification. Embeddings expressing the high dimensional appearance features of a object are acquired by using a convolutional neural network and re-identification of the tracklet is made based on the distance of the embedding vector among the tracklets. At this time, by using the masked image of the object obtained by instance segmentation as the input of the network, it is possible to perform re-identification determination robust to the background change. Further, since the re-identification determination of the tracklet pair is performed based on the distance between the low-dimensional vectors, the increase in the calculation cost due to the re-identification processing is small. Experiments were carried out using MOT16 dataset, which is a public data set, and the effectiveness of this method is shown.

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行動遷移映像における姿勢特徴を中心とする学習を用いた時系列行動認識

鈴木智之，青木義満

精密工学会誌 (精密工学会) 83 ( 12 ) 1156 - 1165 2017.12

Research paper (scientific journal), Joint Work, Accepted

畳み込みニューラルネットワークによる距離学習を用いた動画像人物再同定

加藤直樹，箱崎浩平，田靡雅基，里雄二，古山純子，青木義満

精密工学会誌 (精密工学会) 83 ( 12 ) 1117 - 1124 2017.12

Research paper (scientific journal), Joint Work, Accepted

CNN-based Spine and Cobb Angle Estimator Using Moire Images

Ran CHOI, Kota WATANABE, Hiroaki JINGUJI, Nobuyuki FUJITA, Yoji OGURA, Satoru DEMURA, Toshiaki KOTANI, Kanichiro WADA, Masashi MIYAZAKI, Hideki SHIGEMATSU, Yoshimitsu AOKI

IIEEJ Transactions on Image Electronics and Visual Computing (IIEEJ) 5 ( 2 ) 135 - 144 2017.12

Research paper (scientific journal), Joint Work, Accepted

ラグビー映像解析システムの開発

大内一成、小林大祐、中洲俊信、青木義満

電子情報通信学会通信ソサイエティ和文論文誌 (電子情報通信学会) J100-B ( 12 ) 941 - 951 2017.12

Research paper (scientific journal), Joint Work, Accepted

Unconstrained and Calibration-free Gaze Estimation in a Room-scale Area using a Monocular Camera

Kimimasa Tamura, Ran Choi, Yoshimitsu Aoki

IEEE Access (IEEE) PP ( 99 ) 1 - 12 2017.08

Research paper (scientific journal), Joint Work, Accepted

CNNを用いた姿勢特徴抽出と行動遷移を考慮した人物行動認識

工藤雄太，指田岳彦，青木義満

電子情報通信学会和文論文誌D (電子情報通信学会) J100-D ( 7 ) 681 - 691 2017.07

Research paper (scientific journal), Joint Work, Accepted

Detecting Conversational Group Associates from Static Images

Shoichi Inaba, Yoshimitsu Aoki

Journal of Signal Processing (Research Institute of Signal Processing) 21 ( 4 ) 199 - 202 2017.07

Research paper (scientific journal), Joint Work, Accepted

Behavior Monitoring System by using Vision Sensors

AOKI Yoshimitsu

IEICE Communication Society Magazine (IEICE) 11 ( 1 ) 30 - 38 2017.06

Research paper (scientific journal), Single Work, Accepted

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