A Novel Viewer Counter for Digital Billboards
Duan-Yu Chen, Kuan-Yi Lin · 2009
This paper presents a novel viewer counter for an environment in which a stationary camera can count the number of people watching an electronic billboard without counting the repetitions in real time video streams. The potential buyers actually watching an advertisement or merchandise 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. Our experiment results demonstrate the robustness of the proposed system for the viewer counting task.