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Action recognition by jointly using video proposal and trajectory
Qi, Lei1,2; Lu, Xiaoqiang1; Li, Xuelong1,2
2018-08-27
Conference Name2nd International Conference on Vision, Image and Signal Processing, ICVISP 2018
Source PublicationProceedings of the 2nd International Conference on Vision, Image and Signal Processing, ICVISP 2018
Conference Date2018-08-27
Conference PlaceLas Vegas, NV, United states
PublisherAssociation for Computing Machinery
Contribution Rank1
Abstract

As a popular research field in computer vision community, human action recognition in videos is a challenging task. In recent years, trajectory based methods have been proven effective for action recognition. However, because trajectory is generated around motion region, trajectory based methods often only pay attention to regions with high motion salience in video and ignore motionless but semantic objects. To compensate the shortage of trajectory based methods, video proposal is utilized for its ability to discover semantic object in this paper. In the proposed method, video proposal and trajectory are extracted simultaneously to capture motion information and object information. The proposed method can be divided into three steps: 1) trajectories and video proposals are extracted from video to capture motion information and object information respectively; 2) a trained Convolution Neural Network (CNN) model is employed to describe the extracted trajectories and video proposals; 3) the holistic representation of video is constructed by Fisher Vector model and then input to classifier to get the action label. The complementarity between trajectory and video proposal enables the discrimination power of the proposed method for kinds of actions. The proposed method is evaluated on UCF101 and HMDB51, on which the promising results prove the effectiveness of the proposed method. © 2018 ACM.

Department光学影像学习与分析中心
DOI10.1145/3271553.3271563
Indexed ByEI ; CPCI
ISBN9781450365291
Language英语
WOS IDWOS:000461414900004
EI Accession Number20185106273444
Citation statistics
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31107
Collection光学影像学习与分析中心
Affiliation1.Center for OPTical IMagery Analysis and Learning (OPTIMAL), Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, Shanxi; 710119, China;
2.University of Chinese Academy of Sciences, Beijing; 100049, China
Recommended Citation
GB/T 7714
Qi, Lei,Lu, Xiaoqiang,Li, Xuelong. Action recognition by jointly using video proposal and trajectory[C]:Association for Computing Machinery,2018.
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