TTGSG: Event-Driven Crowd Pattern Recognition with Lightweight and Energy-Efficient Edge Intelligence

Accurate and energy-efficient crowd pattern recognition is important for edge-intelligent monitoring in large public spaces. Existing vision-based methods often require continuous image acquisition and high-bandwidth transmission. Device-based methods may also involve identity-related signals. These limitations make them less suitable for privacy-sensitive and resource-constrained edge scenarios. To address these problems, this paper proposes TTGSG, a target-time guided spatiotemporal graph learning framework for lightweight event-driven crowd pattern recognition. The framework uses a dual-beam infrared sensing scheme. It converts pedestrian movements into compact event-level observations, including crowd count, movement direction, traversal speed, inflow, and outflow. Environmental attributes are also introduced as auxiliary inputs. To model irregular event streams, TTGSG designs a target-time guided learning mechanism. To capture dynamic spatial interactions, it develops a C-diffusion-based bidirectional graph propagation module and a global attention-based topology updating mechanism. In addition, a future-oriented stochastic latent representation is constructed for uncertainty-aware high-density early warning. Experiments on four real-world public-space datasets show that TTGSG achieves better crowd-state recognition and early-warning performance than representative baselines. Edge deployment results further demonstrate that the proposed framework can run efficiently on resource-constrained hardware. These results indicate that TTGSG achieves a practical balance among recognition accuracy, warning timeliness, model compactness, and energy efficiency. It provides a deployable edge AI solution for privacy-preserving event-driven crowd analytics.