Central to the problem of active multi-camera surveillance is the fundamental issue of fairness in the observation of crowds of targets such that no target is “starved” of observation by the cameras for a long time. This paper presents a principled decision-theoretic multi-camera coordination and control (MC²) algorithm called fair-MC² that can coordinate and control the active cameras to achieve max-min fairness in the observation of crowds of targets moving stochastically. Our fair-MC² algorithm is novel in demonstrating how (a) the uncertainty in the locations, directions, speeds, and observation times of the targets arising from the stochasticity of their motion can be modeled probabilistically, (b) the notion of fairness in observing targets can be formally realized in the domain of multi-camera surveillance for the first time by exploiting the max-min fairness metric to formalize our surveillance objective, that is, to maximize the expected minimum observation time over all targets while guaranteeing a predefined image resolution of observing them, and (c) a structural assumption in the state transition dynamics of a surveillance environment can be exploited to improve its scalability to linear time in the number of targets to be observed during surveillance. Empirical evaluation through extensive simulations in realistic surveillance environments shows that fair-MC² outperforms the state-of-the-art and baseline MC² algorithms. We have also demonstrated the feasibility of deploying our fair-MC² algorithm on real AXIS 214 PTZ cameras.