[1]ZHUANG Weiyuan,CHENG Yun,LIN Xianming,et al.Action recognition based on the angle histogram of key parts[J].CAAI Transactions on Intelligent Systems,2015,10(1):20-26.[doi:10.3969/j.issn.1673-4785.201410039]
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Action recognition based on the angle histogram of key parts

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 1 Page number: 20-26 Column: 学术论文—智能系统 Public date: 2015-03-25
Title:
Action recognition based on the angle histogram of key parts
Author(s):
ZHUANG Weiyuan1; 3;  CHENG Yun2;  LIN Xianming1; 3;  SU Songzhi1; 3;  CAO Donglin1; 3;  LI Shaozi1; 3
1. School of Information Science and Technology, Xiamen University, Xiamen 361005, China;
2. Department of Communication and Control Engineering, Hunan University of Humanities, Science and Technology, Loudi 417000, China;
3. Fujian Key Laboratory of the Brain-Like Intelligent Systems, Xiamen 361005, China
Keywords:
angle feature; action recognition; key parts; angle histogram; pose representation; action analyze; action feature
CLC:
TP391.4
DOI:
10.3969/j.issn.1673-4785.201410039
Abstract:
The current pose-based methods usually make a strong assumption for the accuracy of pose, but when the pose analysis is not precise, these methods cannot achieve satisfying results of recognition. Therefore, this paper proposed a low-dimensional and robust descriptor on the gesture feature of the human body based on the angle histogram of key limbs, which is used to map the entire action video into an feature vector. A co-occurrence model is introduced into the feature vector for expressing the relationship among limbs. Finally, a two-layer support vector machine (SVM) classifier is designed. The first layer is used to select highly discriminative limbs as key limbs and the second layer takes angle histogram of key limbs as the feature vector for action recognition. Experiment results demonstrated that the action feature based on angle histogram of key limbs has excellent judgment ability, may properly distinguish similar actions and achieve better recognition effect.
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