A cross-view intelligent person search method based on multi-feature constraints

Person searches aim to simultaneously locate and identify persons to be queried from different scenes, which is crucial for disaster emergency management and public safety. However, the high variability of environmental features in different video scenes, along with the susceptibility of people searching for occlusions or dense populations, results in existing methods suffering from inefficiency and poor accuracy in searching for cross-view persons. Therefore, we propose a cross-view intelligent person search method based on multifeature constraints. First, we establish the global-local context-aware (GLCA) module, which fully extracts the differential personnel features. Second, we construct the semantic complementarity and feature aggregation (SCFA) module for personnel-scale feature constraints in different contexts. Third, we constrain the method in terms of person spatial, person identity, and detection confidence features to improve person search accuracy. Finally, we construct a case experiment dataset, select two public benchmark datasets, and conduct a detailed experimental analysis based on them. The results show that our method can be applied to personnel search tasks in complex scenarios well, and the search results outperform those of 25 other state-of-the-art algorithms, with mAPs improved by 0.41%-19.71%. This approach effectively enhances the informatization level of disaster emergency rescue and public safety management.