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- Title
Mining Key Skeleton Poses with Latent SVM for Action Recognition.
- Authors
Li, Xiaoqiang; Zhang, Yi; Liao, Dong
- Abstract
Human action recognition based on 3D skeleton has become an active research field in recent years with the recently developed commodity depth sensors. Most published methods analyze an entire 3D depth data, construct mid-level part representations, or use trajectory descriptor of spatial-temporal interest point for recognizing human activities. Unlike previous work, a novel and simple action representation is proposed in this paper which models the action as a sequence of inconsecutive and discriminative skeleton poses, named as key skeleton poses. The pairwise relative positions of skeleton joints are used as feature of the skeleton poses which are mined with the aid of the latent support vector machine (latent SVM). The advantage of our method is resisting against intraclass variation such as noise and large nonlinear temporal deformation of human action. We evaluate the proposed approach on three benchmark action datasets captured by Kinect devices: MSR Action 3D dataset, UTKinect Action dataset, and Florence 3D Action dataset. The detailed experimental results demonstrate that the proposed approach achieves superior performance to the state-of-the-art skeleton-based action recognition methods.
- Subjects
SUPPORT vector machines; PATTERN recognition systems; DATA analysis; HUMAN behavior; NONLINEAR theories
- Publication
Applied Computational Intelligence & Soft Computing, 2017, p1
- ISSN
1687-9724
- Publication type
Article
- DOI
10.1155/2017/5861435