Image fusion for object tracking using Factor Graphs

In recent years there has been an increasing demand of systems that automatically manage and control the state of large critical areas, such as airports, harbors, parking lots, etc. The framework of the Bayesian Factor Graphs to target fusion seems to be quite promising with respect to classical approaches because of its modularity and because it can naturally integrate very heterogeneous sources of information. The system presented in this paper fuses real-time data coming from various sensors, along with estimates coming from the tracked object models (if available). All the information is merged within environmental constraints in order to provide the best estimate of the state of a moving object. Factor graphs allow the information to flow bidirectionally, to predict the future, or to strengthen our knowledge of the past. In this paper we focus on camera sensors, deployed along the area of interest. The information is merged into the factor graph after geometric inversion and covariance estimate. The problem of automatic localization of moving objects on the images is also addressed. The framework has been tested on a parking area, where states are estimated, accuracy is assessed and considerations about the framework are provided.