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Tracking by Detection of Multiple Faces using SSD and CNN Features
Do Nhu Tai,Soo-Hyung Kim,Guee-Sang Lee,Hyung-Jeong Yang,In-Seop Na,A-Ran Oh 한국스마트미디어학회 2018 스마트미디어저널 Vol.7 No.4
Multi-tracking of general objects and specific faces is an important topic in the field of computer vision applicable to many branches of industry such as biometrics, security, etc. The rapid development of deep neural networks has resulted in a dramatic improvement in face recognition and object detection problems, which helps improve the multiple-face tracking techniques exploiting the tracking-by-detection method. Our proposed method uses face detection trained with a head dataset to resolve the face deformation problem in the tracking process. Further, we use robust face features extracted from the deep face recognition network to match the tracklets with tracking faces using Hungarian matching method. We achieved promising results regarding the usage of deep face features and head detection in a face tracking benchmark.
Nhu-Tai Do(도누따이),Sung-Taek Jung(정성택),Hyung-Jeong Yang(양형정),Soo-Hyung Kim(김수형) Korean Institute of Information Scientists and Eng 2020 정보과학회논문지 Vol.47 No.2
Knee bone tumor detection plays an essential role in assisting the clinical diagnosis process. To the best of our knowledge, there is no method to integrate end-to-end segmentation and classification for this problem. In this paper, we propose a multi-task deep learning architecture for classification and segmentation of the tumor regions in the knee bone. Also, we introduce multi-level distance masks from the distance transform of tumor region, and these multi-level distance masks have a role as a guided filter in enabling the network to capture semantic data around tumor regions. Besides, the architecture has a regularizing effect on the learning process between segmentation and classification. Our model was evaluated on the Chonnam National University Hospital dataset and achieved good performance compared to other methods.
Tracking by Detection of Multiple Faces using SSD and CNN Features
Tai, Do Nhu,Kim, Soo-Hyung,Lee, Guee-Sang,Yang, Hyung-Jeong,Na, In-Seop,Oh, A-Ran THE KOREAN INSTITUTE OF SMART MEDIA 2018 스마트미디어저널 Vol.7 No.4
Multi-tracking of general objects and specific faces is an important topic in the field of computer vision applicable to many branches of industry such as biometrics, security, etc. The rapid development of deep neural networks has resulted in a dramatic improvement in face recognition and object detection problems, which helps improve the multiple-face tracking techniques exploiting the tracking-by-detection method. Our proposed method uses face detection trained with a head dataset to resolve the face deformation problem in the tracking process. Further, we use robust face features extracted from the deep face recognition network to match the tracklets with tracking faces using Hungarian matching method. We achieved promising results regarding the usage of deep face features and head detection in a face tracking benchmark.
HSFE Network and Fusion Model based Dynamic Hand Gesture Recognition
( Do Nhu Tai ),( In Seop Na ),( Soo Hyung Kim ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.9
Dynamic hand gesture recognition(d-HGR) plays an important role in human-computer interaction(HCI) system. With the growth of hand-pose estimation as well as 3D depth sensors, depth, and the hand-skeleton dataset is proposed to bring much research in depth and 3D hand skeleton approaches. However, it is still a challenging problem due to the low resolution, higher complexity, and self-occlusion. In this paper, we propose a hand-shape feature extraction(HSFE) network to produce robust hand-shapes. We build a hand-shape model, and hand-skeleton based on LSTM to exploit the temporal information from hand-shape and motion changes. Fusion between two models brings the best accuracy in dynamic hand gesture (DHG) dataset.
A TabNet - Based System for Water Quality Prediction in Aquaculture
Trong-Nghia Nguyen,김수형(Soo Hyung Kim),도누따이(Nhu-Tai Do),Thai-Thi Ngoc Hong,양형정(Hyung Jeong Yang),이귀상(Guee Sang Lee) 한국스마트미디어학회 2022 스마트미디어저널 Vol.11 No.2
In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.