基于层次部件树结构的动作识别判决模型<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201710003

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Abstract Action recognition of body pose from static image is exploited in this paper. A hierarchical part tree structure is proposed. In the structure, each node is composed by a collection of poselets to represent its pose variation and pairs of linked nodes are constrained to form a pictorial structure. Grounded on the structure, a discriminatively trained action recognition model based on hierarchical part tree is presented. Except for deforming cost, the pairwise potential function in the model introduces co-occurrence cost. Parent part contains child part and the relative position of linked nodes is described by normal distribution, and thus the matching procedure is inferred efficiently in the framework of distance transform and message passing. Three models with different number of nodes by trimming the tree are comparatively evaluated on two datasets. Experimental results demonstrate that coarse parts in former two layers have strong saliency for action recognition, the recognition capability is further improved by body parts in the third layer, and the anatomical stick parts in the fourth layer are basically not useful for action recognition.

Key words： Action Recognition Pose Poselet Hierarchical Part Tree

Received: 12 May 2017

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61472087), Top-notch Academic Programs Project of Jiangsu Higher Education Institutions(No.PPZY2015A090)

About author:: (QIAN Yinzhong(Corresponding author), born in 1970, Ph.D. candidate, associate professor. His research interests include computer vision, image processing and machine learning.)
(SHEN Yifan, born in 1965, Ph.D., professor. His research interests include compu-ter graphics and computer vision.)

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QIAN Yinzhong,SHEN Yifan. Discriminatively Trained Action Recognition Model Based on Hierarchical Part Tree[J]. , 2017, 30(10): 885-893.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201710003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I10/885

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