Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel approach to a challenging problem of daily life cooking activity recognition task based upon object use and frame sequence tagging. We use a dynamic SVM-HMM hybrid model which combines structural as well as temporal video sequence information to jointly infer the most likely cooking activity labels. We demonstrate that our approach can achieve activity recognition rates for kitchen scenarios of more than 72% on a real-world cooking dataset consisting of 9 cooking activities with significant variations in performance of these activities by different subjects. Such a context based approach as discussed in this paper can be extended to other fine grain activities such as hospital operating rooms in medical practices, agricultural and manufacturing operations, etc. |
Year | Venue | Keywords |
|---|---|---|
2013 | 2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013) | Kitchen, Cooking Activity Recognition, Frame Classification, Kinect |
Field | DocType | ISSN |
Computer vision,Cooking (activity),Activity recognition,Context based,Computer science,Support vector machine,Manufacturing operations,Speech recognition,Artificial intelligence,Frame sequence,Hidden Markov model | Conference | 1522-4880 |
Citations | PageRank | References |
3 | 0.46 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shubham Bansal | 1 | 34 | 1.80 |
| Shubham Khandelwal | 2 | 35 | 2.17 |
| Shubham Gupta | 3 | 278 | 27.57 |
| Dushyant Goyal | 4 | 41 | 5.21 |