UC Merced

We propose a Bayesian framework for modeling and predicting traffic patterns using information obtained from wireless sensor networks. For concreteness, we apply the proposed framework to a smart building application in which traffic patterns of humans are modeled and predicted through detection and matching of their images taken from cameras at different locations. Experiments with more than 4,000 images of 20 subjects demonstrate promising results in traffic pattern prediction using the proposed algorithm. The algorithm can also be applied to other applications including surveillance, traffic monitoring, abnor- mality detection, and location-based services. In addition, the long-term deployment of the network can be used for security, energy conservation and utilization improvement of smart buildings.