  This paper investigates vision-based cooperative estimation of a 3D target
object pose for visual sensor networks. In our previous works, we presented an
estimation mechanism called networked visual motion observer achieving
averaging of local pose estimates in real time. This paper extends the
mechanism so that it works even in the presence of cameras not viewing the
target due to the limited view angles and obstructions in order to fully take
advantage of the networked vision system. Then, we analyze the averaging
performance attained by the proposed mechanism and clarify a relation between
the feedback gains in the algorithm and the performance. Finally, we
demonstrate the effectiveness of the algorithm through simulation.
