DGNet: A Double-Graph Framework Combined with Occluded Person Re-Identification for the Prediction of Pedestrian Flow in Scenic Spots

Predicting pedestrian flow in scenic spots is a critical challenge for managing tourist areas. To address this, we propose the double-graph network (DGNet) framework, which combines occluded person re-identification and pedestrian flow prediction. Scenic spots are represented as nodes in a graph, with their pedestrian flow as node attributes, naturally forming a graph structure. DGNet consists of two key graphs: CNN-transformer graph (CTG) for occluded person re-identification and spatial-temporal graph (STG) for pedestrian flow prediction. CTG integrates global and local features using CNN, Transformer, and graph convolutional network (GCN) to handle occlusions effectively. STG employs spatial-temporal attention mechanisms to extract correlations across time and space for accurate pedestrian flow prediction. Based on comprehensive experiments, the proposed CTG obtains a comparable performance to the current mainstream occluded person re-identification algorithms. Comparative experiments with other models show that the STG achieves the best results on MAE, RMSE and MAPE metrics, outperforming other models by at least 0.29%, 0.51%, and 0.52%. These results highlight the framework’s robustness and accuracy. Moreover, the novelty of DGNet lies in its ability to bridge occluded person re-identification with flow prediction tasks, offering a scalable solution applicable to diverse scenic spots. This work provides practical insights into leveraging video surveillance for effective crowd management in tourist areas.