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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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/ |
work_keys_str_mv |
AT tingxinren videobasedhumanmotioncapturedataretrievalviamotionsetnetwork AT weili videobasedhumanmotioncapturedataretrievalviamotionsetnetwork AT zifeijiang videobasedhumanmotioncapturedataretrievalviamotionsetnetwork AT xueqingli videobasedhumanmotioncapturedataretrievalviamotionsetnetwork AT yanhuang videobasedhumanmotioncapturedataretrievalviamotionsetnetwork AT jingliangpeng videobasedhumanmotioncapturedataretrievalviamotionsetnetwork |
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