In order to improve the efficiency of pedestrian retrieval and re-identification with numerous surveillance cameras, a novel multi-camera dynamic logical topology inference method is proposed, which includes:(1) A conditional probability graph convolution network(CPG) is designed, which samples and aggregates information of multi-order neighbor nodes according to the conditional probability. The CPG is employed to aggregate the influence of all nodes relative to the target node and calculate the global correlation between each node.(2) A dynamic spatio-temporal information aggregation model(STIA) in a multi-camera system is proposed. The dynamic logical topology of the multi-camera system is inferred based on the pedestrian’s walking direction and the spatio-temporal factors.(3) A novel correlation indicator in multi-camera system is proposed. This indicator detects and quantifies temporal and causal relationships within and across camera views by a designed time-delayed Jensen-Shannon divergence(TDJS). It can be used to measure the causal correlation between two camera nodes over a long time delay. Some ablation studies and simulation experiments are performed on a dataset collected from real scenes show that our method can efficiently infer the logical topology of multiple cameras.