The complex action recognition via the correlated topic model.
Human complex action recognition is an important research area of the action recognition. Among various obstacles to human complex action recognition, one of the most challenging is to deal with self-occlusion, where one body part occludes another one. This paper presents a new method of human compl...
| Publicado en: | Scientific World Journal pp. 810185 - 810186 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103811827&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103811827 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 103811827 103811827 NLM24574920 2012490083 10.1155/2014/810185 NLM24574920 PMC3915526 103811827 ppf: 810185 ppct: 1 formats: tig: atl: The complex action recognition via the correlated topic model. aug: au: Tu, Hong-Bin Xia, Li-Min Wang, Zheng-Wu affil: School of Information Science and Engineering, Central South University, ChangSha, Hunan 410075, China. sug: subj: Models, Biological Visual Perception Spatial Perception ab: Human complex action recognition is an important research area of the action recognition. Among various obstacles to human complex action recognition, one of the most challenging is to deal with self-occlusion, where one body part occludes another one. This paper presents a new method of human complex action recognition, which is based on optical flow and correlated topic model (CTM). Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms of an occlusion state variable. Secondly, the structure from motion (SFM) is used for reconstructing the missing data of point trajectories. Then, we can extract the key frame based on motion feature from optical flow and the ratios of the width and height are extracted by the human silhouette. Finally, we use the topic model of correlated topic model (CTM) to classify action. Experiments were performed on the KTH, Weizmann, and UIUC action dataset to test and evaluate the proposed method. The compared experiment results showed that the proposed method was more effective than compared methods. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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