Name
Abstract | ||
|---|---|---|
Many videos depict people, and it is their interactions that inform us of their activities, relation to one another and the cultural and social setting. With advances in human action recognition, researchers have begun to address the automated recognition of these human-human interactions from video. The main challenges stem from dealing with the considerable variation in recording settings, the appearance of the people depicted and the performance of their interaction. This survey provides a summary of these challenges and datasets, followed by an in-depth discussion of relevant vision-based recognition and detection methods. We focus on recent, promising work based on convolutional neural networks (CNNs). Finally, we outline directions to overcome the limitations of the current state-of-the-art. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Convolutional neural network,Computer science,Action recognition,Human–computer interaction,Artificial intelligence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1808.00022 | 1 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandros Stergiou | 1 | 3 | 1.06 |
| Ronald Poppe | 2 | 1083 | 49.93 |