Prediction-Aware UAV Swarm Crowd Surveillance: Balancing Coverage and Recognition Accuracy

UAV swarms provide a flexible sensing platform for smart-city crowd surveillance, but cooperative aerial monitoring remains challenging due to dynamic pedestrian distributions, partial observability, and the trade-off between visual coverage and recognition accuracy. In particular, flying at higher altitudes increases the field of view but reduces recognition accuracy, while low-altitude flight improves visual quality at the cost of limited coverage. To address these challenges, this paper proposes an environment-aware cooperative navigation framework that integrates spatiotemporal density prediction with multi-agent reinforcement learning. The surveillance area is modeled as a spatiotemporal graph, where sparse and partial UAV observations are used to predict future pedestrian-density maps and confidence intervals. The predicted density and uncertainty, together with empirical recognition error, UAV position, flight height, battery state, and historical observations, are incorporated into MARL-based policy learning. The learned policy enables UAVs to cooperatively adjust movement and altitude decisions under the centralized training and decentralized execution paradigm. Extensive simulations in UAV-based crowd surveillance environments demonstrate that the proposed framework achieves a more favorable coverage–error trade-off than representative heuristic, prediction-based, single-agent reinforcement learning, and multi-agent reinforcement learning baselines. The results show that prediction-aware and accuracy-aware cooperation improves pedestrian-level surveillance performance under dynamic and partially observable crowd distributions.