In this paper, we present a methodology for refining the segmentation of human silhouettes in indoor videos acquired by fisheye cameras. This methodology is based on a fisheye camera model that employs a spherical optical element and central projection. The parameters of the camera model are determined only once (during calibration), using the correspondence of a number of user-defined landmarks, both in real world coordinates and on a captured video frame. Subsequently, each pixel of the video frame is inversely mapped to the direction of view in the real world and the relevant data are stored in look-up tables for fast utilization in real-time video processing. The proposed fisheye camera model enables the inference of possible real world positions and conditionally the height and width of a segmented cluster of pixels in the video frame. In this work we utilize the proposed calibrated camera model to achieve a simple geometric reasoning that corrects gaps and mistakes of the human figure segmentation, detects segmented human silhouettes inside and outside the room and rejects segmentation that corresponds to non-human activity. Unique labels are assigned to each refined silhouette, according to their estimated real world position and appearance and the trajectory of each silhouette in real world coordinates is estimated. Experimental results are presented for a number of video sequences, in which the number of false positive pixels (regarding human silhouette segmentation) is substantially reduced as a result of the application of the proposed geometry-based segmentation refinement. 
Refinement of human silhouette segmentation in omni-directional indoor videos