Video-Based Human Motion Capture Data Retrieval via MotionSet Network

Content-based human motion capture (MoCap) data retrieval facilitates reusing motion data that have already been captured and stored in a database. For a MoCap data retrieval system to get practically deployed, both high precision and natural interface are demanded. Targeting both, we propose a vide...

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Main Authors: Tingxin Ren, Wei Li, Zifei Jiang, Xueqing Li, Yan Huang, Jingliang Peng
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9220910/
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spelling doaj-e93341fdea4b4a78918b684540c9fa132021-03-30T04:40:27ZengIEEEIEEE Access2169-35362020-01-01818621218622110.1109/ACCESS.2020.30302589220910Video-Based Human Motion Capture Data Retrieval via MotionSet NetworkTingxin Ren0Wei Li1https://orcid.org/0000-0002-9362-1759Zifei Jiang2Xueqing Li3Yan Huang4Jingliang Peng5School of Software, Shandong University, Jinan, ChinaSchool of Software, Shandong University, Jinan, ChinaSchool of Software, Shandong University, Jinan, ChinaSchool of Software, Shandong University, Jinan, ChinaSchool of Software, Shandong University, Jinan, ChinaShandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, ChinaContent-based human motion capture (MoCap) data retrieval facilitates reusing motion data that have already been captured and stored in a database. For a MoCap data retrieval system to get practically deployed, both high precision and natural interface are demanded. Targeting both, we propose a video-based human MoCap data retrieval solution in this work. It lets users to specify a query via a video clip, addresses the representational gap between video and MoCap clips and extracts discriminative motion features for precise retrieval. Specifically, the proposed scheme firstly converts each video clip or MoCap clip at a certain viewpoint to a binary silhouette sequence. Regarding a video or MoCap clip as a set of silhouette images, the proposed scheme uses a convolutional neural network, named MotionSet, to extract the discriminative motion feature of the clip. The extracted motion features are used to match a query to repository MoCap clips for the retrieval. Besides the algorithmic solution, we also contribute a human MoCap dataset and a human motion video dataset in couple that contain various action classes. Experiments show that our proposed scheme achieves an increase of around 0.25 in average MAP and costs about 1/26 time for online retrieval, when compared with the benchmark algorithm.https://ieeexplore.ieee.org/document/9220910/MotionSetmotion capture data retrievalconvolutional neural networkdeep learning
collection DOAJ
language English
format Article
sources DOAJ
author Tingxin Ren
Wei Li
Zifei Jiang
Xueqing Li
Yan Huang
Jingliang Peng
spellingShingle Tingxin Ren
Wei Li
Zifei Jiang
Xueqing Li
Yan Huang
Jingliang Peng
Video-Based Human Motion Capture Data Retrieval via MotionSet Network
IEEE Access
MotionSet
motion capture data retrieval
convolutional neural network
deep learning
author_facet Tingxin Ren
Wei Li
Zifei Jiang
Xueqing Li
Yan Huang
Jingliang Peng
author_sort Tingxin Ren
title Video-Based Human Motion Capture Data Retrieval via MotionSet Network
title_short Video-Based Human Motion Capture Data Retrieval via MotionSet Network
title_full Video-Based Human Motion Capture Data Retrieval via MotionSet Network
title_fullStr Video-Based Human Motion Capture Data Retrieval via MotionSet Network
title_full_unstemmed Video-Based Human Motion Capture Data Retrieval via MotionSet Network
title_sort video-based human motion capture data retrieval via motionset network
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description Content-based human motion capture (MoCap) data retrieval facilitates reusing motion data that have already been captured and stored in a database. For a MoCap data retrieval system to get practically deployed, both high precision and natural interface are demanded. Targeting both, we propose a video-based human MoCap data retrieval solution in this work. It lets users to specify a query via a video clip, addresses the representational gap between video and MoCap clips and extracts discriminative motion features for precise retrieval. Specifically, the proposed scheme firstly converts each video clip or MoCap clip at a certain viewpoint to a binary silhouette sequence. Regarding a video or MoCap clip as a set of silhouette images, the proposed scheme uses a convolutional neural network, named MotionSet, to extract the discriminative motion feature of the clip. The extracted motion features are used to match a query to repository MoCap clips for the retrieval. Besides the algorithmic solution, we also contribute a human MoCap dataset and a human motion video dataset in couple that contain various action classes. Experiments show that our proposed scheme achieves an increase of around 0.25 in average MAP and costs about 1/26 time for online retrieval, when compared with the benchmark algorithm.
topic MotionSet
motion capture data retrieval
convolutional neural network
deep learning
url https://ieeexplore.ieee.org/document/9220910/
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AT xueqingli videobasedhumanmotioncapturedataretrievalviamotionsetnetwork
AT yanhuang videobasedhumanmotioncapturedataretrievalviamotionsetnetwork
AT jingliangpeng videobasedhumanmotioncapturedataretrievalviamotionsetnetwork
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