A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Environment mapping and other applications of world projections
IEEE Computer Graphics and Applications
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure from Motion with Wide Circular Field of View Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
Plane-Based calibration and auto-calibration of a fish-eye camera
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Visualization and Computer Graphics
Near real-time human silhouette and movement detection in indoor environments using fixed cameras
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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In this paper, we concentrate on refining the results of segmenting human presence from indoors videos acquired by a fisheye camera, using a 3D mathematical model of the camera. The model has been calibrated according to the specific indoor environment that is being monitored. Human segmentation is implemented using a standard established technique. The fisheye camera used for video acquisition is modeled using a spherical element, while the parameters of the camera model are determined only once, using the correspondence of a number of user-defined landmarks, both in real world coordinates and on the acquired 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 very fast utilization in real-time video processing. The proposed fisheye camera model enables the inference of possible real world positions of a segmented cluster of pixels in the video frame. In this work, we utilize the constructed camera model to achieve a simple geometric reasoning that corrects gaps and mistakes of the human figure segmentation. Initial results are also presented for a small number of video sequences, which prove the efficiency of the proposed method.