Name
Abstract | ||
|---|---|---|
This paper introduces a visual-based system, which can count the number of viewers and recognize their gender in front of an electronic billboard in real-time video streams. The viewers actually watching an advertisement are captured via frontal face detection techniques. To count the number of viewer precisely, the problem of occlusions between viewers is tackled. Besides, a complementary set of features is extracted from the torso of a viewer due to the fact that the part of the body contains relatively rich discriminative information than other body parts. In addition, for conducting robust viewer recognition, an online classifier trained by AdaBoost is developed. To recognize the gender of the counted viewers, an approach based on spatiotemporal probabilistic framework is proposed. Our experimental results demonstrate the robustness of the proposed system for the viewer counting and gender recognition tasks. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s11042-011-0746-9 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Viewer counting,Gender recognition,Electronic billboard | Computer vision,Torso,AdaBoost,Pattern recognition,Computer science,Robustness (computer science),Electronics,Artificial intelligence,Face detection,Classifier (linguistics),Discriminative model,Probabilistic framework | Journal |
Volume | Issue | ISSN |
59 | 1 | 1380-7501 |
Citations | PageRank | References |
0 | 0.34 | 30 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Duan-Yu Chen | 1 | 296 | 28.79 |
| Kuan-Yi Lin | 2 | 6 | 1.77 |