Name
Title | ||
|---|---|---|
Leveraging Skeleton Structure And Time Dependencies In The Scope Of Action Recognition |
Abstract | ||
|---|---|---|
In this work, the structure of the moving skeleton, which is a time varying graph, along with the temporal dependencies of human action were leveraged in the scope of skeleton action recognition. The optimization of the proposed model shares similarities with the optimization problem of Slow Feature Analysis (SFA) enabling a well defined solution. Moreover, due to the incorporated skeleton structure, the learned slow functions enclose information regarding the geometry of the skeleton movement which is very useful in the action recognition problem. Two skeleton action datasets were used to evaluate our method, the MSR Action 3D and a dataset whose actions were inspired by psychological studies. Both datasets were captured by depth cameras. The proposed method yielded promising results when evaluated on the aforementioned datasets. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Skeleton Tracking, Action Recognition, Slow Feature Analysis, Human Activity |
Field | DocType | ISSN |
Computer vision,Graph,Pattern recognition,Noise measurement,Computer science,Action recognition,Feature extraction,Artificial intelligence,Skeleton (computer programming),Optimization problem,Pattern recognition (psychology) | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ioannis Tsingalis | 1 | 0 | 0.34 |
| Nicholas Vretos | 2 | 33 | 12.21 |
| Petros Daras | 3 | 1129 | 131.72 |