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High-Movement human segmentation in Video Using Adaptive N-Frames Ensemble

Computers, Materials & Continua 2022年第12期73卷 4743-4762页

作者： Yong-Woon Kim Yung-Cheol Byun Dong Seog Han Dalia Dominic Sibu Cyriac Centre for Digital Innovation CHRIST(Deemed to be University)Bangalore560029India Department of Computer Engineering Jeju National UniversityJeju63243Korea School of Electronics Engineering Kyungpook National UniversityDaegu41566Korea

Awide range of camera apps and online video conferencing services support the feature of changing the background in real-time for aesthetic,privacy,and security *** studies show that theDeep-Learning(DL)is a suitable option for human segmentation,and the ensemble of multiple DL-based segmentation models can improve the segmentation ***,these approaches are not as effective when directly applied to the image segmentation in a *** paper proposes an Adaptive N-Frames Ensemble(AFE)approach for high-movement human segmentation in a video using an ensemble of multiple DL *** contrast to an ensemble,which executes multiple DL models simultaneously for every single video frame,the proposed AFE approach executes only a single DL model upon a current video *** combines the segmentation outputs of previous frames for the final segmentation output when the frame difference is less than a particular *** method employs the idea of the N-Frames Ensemble(NFE)method,which uses the ensemble of the image segmentation of a current video frame and previous video ***,NFE is not suitable for the segmentation of fast-moving objects in a video nor a video with low frame *** proposed AFE approach addresses the limitations of the NFE *** experiment uses three human segmentation models,namely Fully Convolutional Network(FCN),DeepLabv3,and *** evaluated our approach using 1711 videos of the TikTok50f dataset with a single-person *** TikTok50f dataset is a reconstructed version of the publicly available TikTok dataset by cropping,resizing and dividing it into videos having 50 frames *** paper compares the proposed AFE with single models and the Two-Models Ensemble,as well as the NFE *** experiment results show that the proposed AFE is suitable for low-movement as well as high-movement human segmentation in a video.

关键词： High movement human segmentation artificial intelligence deep learning ensemble video instance segmentation

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Real-time human segmentation by BowtieNet and a SLAM-based human AR system

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Virtual Reality & Intelligent Hardware 2019年第5期1卷 511-524页

作者： Xiaomei ZHAO Fulin TANG Yihong WU National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Background Generally, it is difficult to obtain accurate pose and depth for a non-rigid moving object from a single RGB camera to create augmented reality (AR). In this study, we build an augmented reality system from a single RGB camera for a non-rigid moving human by accurately computing pose and depth, for which two key tasks are segmentation and monocular Simultaneous Localization and Mapping (SLAM). Most existing monocular SLAM systems are designed for static scenes, while in this AR system, the human body is always moving and non-rigid. Methods In order to make the SLAM system suitable for a moving human, we first segment the rigid part of the human in each frame. A segmented moving body part can be regarded as a static object, and the relative motions between each moving body part and the camera can be considered the motion of the camera. Typical SLAM systems designed for static scenes can then be applied. In the segmentation step of this AR system, we first employ the proposed BowtieNet, which adds the atrous spatial pyramid pooling (ASPP) of DeepLab between the encoder and decoder of SegNet to segment the human in the original frame, and then we use color information to extract the face from the segmented human area. Results Based on the human segmentation results and a monocular SLAM, this system can change the video background and add a virtual object to humans. Conclusions The experiments on the human image segmentation datasets show that BowtieNet obtains state-of-the-art human image segmentation performance and enough speed for real-time segmentation. The experiments on videos show that the proposed AR system can robustly add a virtual object to humans and can accurately change the video background.

关键词： Augmented reality Moving object Reconstruction and tracking Camera pose human segmentation

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Image-based clothes changing system

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Computational Visual Media 2017年第4期3卷 337-346页

作者： Zhao-Heng Zheng Hao-Tian Zhang Fang-Lue Zhang Tai-Jiang Mu Computer Science and Engineering University of Michigan Computer Science Department Stanford University School of Engineering and Computer Science Victoria University of Wellington TNList Tsinghua University

Current image-editing tools do not match up to the demands of personalized image manipulation,one application of which is changing clothes in usercaptured images. Previous work can change single color clothes using parametric human warping *** this paper, we propose an image-based clothes changing system, exploiting body factor extraction and content-aware image warping. Image segmentation and mask generation are first applied to the user ***, we determine joint positions via a neural network. Then, body shape matching is performed and the shape of the model is warped to the user's shape. Finally, head swapping is performed to produce realistic virtual results. We also provide a supervision and labeling tool for refinement and further assistance when creating a dataset.

关键词： clothing try-on image warping human segmentation

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