Probabilistic-Topological Calibration of Widely Distributed Camera Networks
We propose a method for estimating the topology of distributed cameras, which can provide useful information for multi-target tracking in a wide area, without object identification among the FOVs of the cameras. In our method, each camera first detects objects in its observed images independently in order to obtain the positions/times where/when the objects enter/exit its FOV. Each obtained data is tentatively paired with all other data detected before the data is observed. A transit time between each paired data and their x-y coordinates are then computed. Based on classifying the distribution of the transit times and the x-y coordinates, object routes between FOVs can be detected. The classification is achieved by simple and robust vector quantization. The detected routes are then categorized to acquire the probabilistic-topological information of distributed cameras. In addition, offline tracking of observed objects can be realized by means of the calibration process. Experiments demonstrated that our method could automatically estimate the topological relationships of the distributed cameras and the object transits among them.