Name
Abstract | ||
|---|---|---|
In this paper we propose a view based approach to recognize humans when engaged in some activity. The width of the outer contour of the binarized silhouette of a walking person is chosen as the image feature. A set of exemplars that occur during an activity cycle is chosen for each individual. Us- ing these exemplars a lower dimensional Frame to Exemplar Distance (FED) vector is generated. A continuous HMM is trained using several such FED vector sequences. This methodology serves to compactly capture structural and dy- namic features that are unique to an individual. The statisti- cal nature of the HMM renders overall robustness to repre- sentation and recognition. Human identification performance of the proposed scheme is found to be quite good when tested on outdoor video sequences collected using surveillance cam- eras. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ICASSP.2002.5745449 | ICASSP |
Keywords | Field | DocType |
hidden markov models,image features,computational modeling,image recognition | Pattern recognition,Computer science,Silhouette,View based,Speech recognition,Robustness (computer science),Artificial intelligence,Hidden Markov model | Conference |
Volume | ISSN | ISBN |
4 | 1520-6149 | 0-7803-7402-9 |
Citations | PageRank | References |
25 | 2.94 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amit Kale | 1 | 708 | 48.47 |
| Naresh P. Cuntoor | 2 | 769 | 46.67 |
| Chellappa, R. | 3 | 13050 | 1440.56 |