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| Content Provider | ACM Digital Library |
|---|---|
| Author | Chen, Hong Mei, Shuli Du, Liqiang Wang, Qing |
| Abstract | In this paper, we present a novel approach for real-time human action recognition using local joints structure and body part locations. Individual body part locations are global features that ignore the local structure information of the human body joints, which is also essential for accurate action recognition. To cope with this problem, we propose local joints structure as a complement, and combine the two features for posture description in our method. We then perform classification using a combination of dynamic time warping, Fourier temporal pyramid representation and linear SVM. Experiments results on three action datasets show that the proposed representation outperforms many existing skeletal representations. |
| Starting Page | 293 |
| Ending Page | 298 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450346924 |
| DOI | 10.1145/3013971.3013974 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-12-03 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Human action recognition Local joints structure Body part locations Svms |
| Content Type | Text |
| Resource Type | Article |
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