Self Calibrating Visual Sensor Networks

This paper presents an unsupervised data driven scheme to automatically estimate the relative topology of overlapping cameras in a large visual sensor network. The proposed method learns the camera topology by employing the statistics of co-occurring observations (of moving targets) in each sensor. Since target observation data is typically very noisy in realistic scenarios, an efficient two step method is used for robust estimation of the planar homography between camera views. In the first step, modes in the co-occurrence data are learned using meanshift. In the second step, a RANSAC based procedure is used to estimate the homography from weighted co-occurrence modes. Note that the first step not only lessens the effects of noise but also reduces the search space for efficient calculation. Unlike most existing algorithms for overlapping camera calibration, the proposed method uses an update mechanism to adapt online to the changes in network topology. The method does not assume prior knowledge about the scene, target, or network properties. It is also robust to noise, traffic intensity, and the amount of overlap between the fields of view. Experiments and quantitative evaluation using both synthetic and real data are presented to support the above claims.