Cooperative autonomous systems are a priority objective for military research \& development of unmanned vehicles. Drone teams are one of the most prominent applications of cooperative unmanned systems and represent an ideal solution for providing both autonomous detection and recognition within security surveillance and monitoring. Here, a drone team may be arranged as a mobile and cooperative sensor network, whose coordination mechanism shall ensure real-time reconfiguration and sensing task balancing within the team. This work proposes a dynamic and decentralized mission planner of a drone team to attain a cooperative behaviour concerning detection and recognition for security surveillance. The design of the planner exploits multi-agent task allocation and game theory, and is based on the theory of learning in games to implement a scalable and resilient system. Model-in-the-loop simulation results are reported to validate the effectiveness of the proposed approach.