Name
Title | ||
|---|---|---|
A structured multi-feature representation for recognizing human action and interaction. |
Abstract | ||
|---|---|---|
Active research has been carried out for human action recognition using 3D human skeleton joints with the release of cost-efficient RGB-D sensors. However, extracting discriminative features from noisy skeleton sequences to effectively distinguish various human action or interaction categories still remains challenging. This paper proposes a structured multi-feature representation for human action and interaction recognition. Specifically, a novel kernel enhanced bag of semantic words (BSW) is designed to represent the dynamic property of skeleton trajectories. By aggregating BSW with the geometric feature, a GBSW representation is constructed for human action recognition. For human interaction recognition where the cooperation of each subject matters, a GBSWC representation is proposed via combining the GBSW feature with a correlation feature which addresses the intrinsic relationship between interactive persons. Experimental results on several human action and interaction datasets demonstrate the superior performances of the proposed features over the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neucom.2018.08.066 | Neurocomputing |
Keywords | Field | DocType |
Action recognition,Interaction recognition,RGB-D sensors,Skeleton joints,Multi-feature | Kernel (linear algebra),Pattern recognition,Action recognition,Human skeleton,Human interaction,RGB color model,Artificial intelligence,Discriminative model,Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
318 | 0925-2312 | 1 |
PageRank | References | Authors |
0.36 | 43 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bangli Liu | 1 | 11 | 2.85 |
| Zhaojie Ju | 2 | 284 | 48.23 |
| Honghai Liu | 3 | 1974 | 178.69 |