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Motion boundary based sampling and 3D co-occurrence descriptors for action recognition

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成果类型:
期刊论文
作者:
Peng, Xiaojiang;Qiao, Yu*;Peng, Qiang
通讯作者:
Qiao, Yu
作者机构:
[Peng, Qiang; Peng, Xiaojiang] Southwest Jiaotong Univ, Chengdu, Peoples R China.
[Peng, Xiaojiang] Hengyang Normal Univ, Dept Comp Sci, Hengyang, Peoples R China.
[Qiao, Yu; Peng, Xiaojiang] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen Key Lab CVPR, Shenzhen, Peoples R China.
[Qiao, Yu] Chinese Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China.
[Qiao, Yu] 1068 Xueyuan Ave, Shenzhen, Peoples R China.
通讯机构:
[Qiao, Yu] 1
1068 Xueyuan Ave, Shenzhen, Peoples R China.
语种:
英文
关键词:
Dense trajectory;Action recognition;3D co-occurrence descriptors;Motion boundary;Bag of Features
期刊:
Image and Vision Computing
ISSN:
0262-8856
年:
2014
卷:
32
期:
9
页码:
616-628
基金类别:
construct program of the key discipline in Hunan province; Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [91320101, 60972111]; Shenzhen Basic Research Program [JC201005270350A, JCYJ20120903092050890, JCYJ20120617114614438]; 100 Talents Program of CASChinese Academy of Sciences; Guangdong Innovative Research Team Program [201001D0104648280]
机构署名:
本校为其他机构
院系归属:
计算机科学与技术学院
摘要:
Recent studies witness the success of Bag-of-Features (BoF) frameworks for video based human action recognition. The detection and description of local interest regions are two fundamental problems in BoF framework. In this paper, we propose a motion boundary based sampling strategy and spatial-temporal (3D) co-occurrence descriptors for action video representation and recognition. Our sampling strategy is partly inspired by the recent success of dense trajectory (DT) based features [Wang et al., 2013] for action recognition. Compared with DT, ...

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