Name
Abstract | ||
|---|---|---|
We revisit the study of a wrist-mounted camera system (referred to as HandCam) for recognizing activities of hands. HandCam has two unique properties as compared to egocentric systems (referred to as HeadCam): (1) it avoids the need to detect hands; (2) it more consistently observes the activities of hands. By taking advantage of these properties, we propose a deep-learning-based method to recognize hand states (free v.s. active hands, hand gestures, object categories), and discover object categories. Moreover, we propose a novel two-streams deep network to further take advantage of both HandCam and HeadCam. We have collected a new synchronized HandCam and HeadCam dataset with 20 videos captured in three scenes for hand states recognition. Experiments show that our HandCam system consistently outperforms a deep-learning-based HeadCam method (with estimated manipulation regions) and a dense-trajectory-based HeadCam method in all tasks. We also show that HandCam videos captured by different users can be easily aligned to improve free v.s. active recognition accuracy (3.3% improvement) in across-scenes use case. Moreover, we observe that finetuning Convolutional Neural Network consistently improves accuracy. Finally, our novel two-streams deep network combining HandCam and HeadCam features achieves the best performance in four out of five tasks. With more data, we believe a joint HandCam and HeadCam system can robustly log hand states in daily life. |
Year | Venue | DocType |
|---|---|---|
2015 | arXiv: Computer Vision and Pattern Recognition | Journal |
Volume | Citations | PageRank |
abs/1512.01881 | 0 | 0.34 |
References | Authors | |
23 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| chengsheng chan | 1 | 4 | 1.08 |
| shouzhong chen | 2 | 3 | 0.72 |
| peixuan xie | 3 | 3 | 0.72 |
| chiungchih chang | 4 | 3 | 0.72 |
| Min Sun | 5 | 1083 | 59.15 |