Machines

This paper presents a novel people counting system for an environment in which a stationary camera can count the number of people watching a TV-wall advertisement or an electronic billboard without counting the repetitions in video streams in real time. The people actually watching an advertisement are identified via frontal face detection techniques. To count the number of people precisely, a complementary set of features is extracted from the torso of a human subject, as that part of the body contains relatively richer information than the face. In addition, for conducting robust people recognition, an online boosted classifier trained by Fisher’s Linear Discriminant (FLD) strategy is developed. Our experiment results demonstrate the efficacy of the proposed system for the people counting task