Name
Abstract | ||
|---|---|---|
•Action-independent Gaussian mixture model (AIGMM) is constructed to preserve local similarity among fine-grained actions.•We propose an approach to handle variable-length patterns of fine-grained actions using the statistics of trained AIGMM.•Efficacy of our approach is demonstrated on 4 fine-grained action datasets, namely, MERL, JIGSAWS, KSCGR, & MPII cooking2. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patcog.2021.108282 | Pattern Recognition |
Keywords | DocType | Volume |
Fine-grained action recognition,Spatio-temporal features,Gaussian mixture model,Dynamic kernels | Journal | 122 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sravani Yenduri | 1 | 0 | 0.34 |
| Nazil Perveen | 2 | 0 | 0.34 |
| Vishnu Chalavadi | 3 | 1 | 2.06 |
| C. Krishna Mohan | 4 | 124 | 17.83 |