Abstract
Recently, 'entry/exit' events of objects in the field-of-views of cameras were used to learn the topology of the camera network. The integration of object appearance was also proposed to employ the visual information provided by the imaging sensors. A problem with these methods is the lack of robustness to appearance changes. This paper integrates face recognition in the statistical model to better estimate the correspondence in the time-varying network. The statistical dependence between the entry and exit nodes indicates the connectivity and traffic patterns of the camera network, which are represented by a weighted directed graph and transition time distributions. A nine-camera network with 25 nodes is analyzed both in simulation and in real-life experiments, and compared with the previous approaches.
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