This paper studies active perception in an urban scenario, focusing on the cooperation between a set of surveillance cameras and mobile robots. The fixed cameras provide a global but incomplete and possibly inaccurate view of the environment, which can be enhanced by a robot's local sensors. Active perception means that the robot considers the effects of its actions on its sensory capabilities. In particular, it tries to improve its sensors' performance, for instance by pointing a pan-and-tilt camera. In this paper, we present a decision-theoretic approach to cooperative active perception, by formalizing the problem as a Partially Observable Markov Decision Process (POMDP). POMDPs provide an elegant way to model the interaction of an active sensor with its environment. The goal of this paper is to provide first steps towards an integrated decision-theoretic approach of cooperative active perception.