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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ogaki, K. Kitani, K.M. Sugano, Y. Sato, Y. |
| Copyright Year | 2012 |
| Description | Author affiliation: Carnegie Mellon University, Pittuburgh, USA (Kitani, K.M.) || The University of Tokyo, Tokyo, Japan (Ogaki, K.; Sugano, Y.; Sato, Y.) |
| Abstract | We focus on the use of first-person eye movement and ego-motion as a means of understanding and recognizing indoor activities from an “inside-out” camera system. We show that when eye movement captured by an inside looking camera is used in tandem with ego-motion features extracted from an outside looking camera, the classification accuracy of first-person actions can be improved. We also present a dataset of over two hours of realistic indoor desktop actions, including both eye tracking information and a high quality outside camera video. We run experiments and show that our joint feature is effective and robust over multiple users. |
| Starting Page | 1 |
| Ending Page | 7 |
| File Size | 242111 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781467316118 |
| ISSN | 21607508 |
| e-ISBN | 9781467316125 |
| e-ISBN | 9781467316101 |
| DOI | 10.1109/CVPRW.2012.6239188 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-16 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Histograms Visualization Quantization Cameras Feature extraction Joints |
| Content Type | Text |
| Resource Type | Article |
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