Abstract We generalize the network flflow formulation for multiobject tracking to multi-camera setups. In the past, reconstruction of multi-camera data was done as a separate extension. In this work, we present a combined maximum a posteriori (MAP) formulation, which jointly models multicamera reconstruction as well as global temporal data association. A flflow graph is constructed, which tracks objects in 3D world space. The multi-camera reconstruction can be effificiently incorporated as additional constraints on the flflow graph without making the graph unnecessarily large. The fifinal graph is effificiently solved using binary linear programming. On the PETS 2009 dataset we achieve results that signifificantly exceed the current state of the art.