Spatio-Temporal Optimization for Foreground/Backgroud Segmentation

We introduce a procedure for calibrated multi camera setups in which observed persons within a realistic and, thus, difficult surrounding are determined as foreground in image sequences via a fully automatic purely data driven segmentation. In order to gain an optimal separation of fore-and background for each frame in terms of Expectation Maximization (EM), an algorithm is proposed which utilizes a combination of geometrical constraints of the scene and, additionally, temporal constraints for a optimization over the entire sequence to estimate the background. This background information is then used to determine accurate silhouettes of the foreground. We demonstrate the effectiveness of our approach based on a qualitative data analysis and compare it to other state of the art approaches.