View-aware Decomposition and Unification for Fast Ground-to-Aerial Person Search

Abstract

Ground-to-aerial person search leverages cooperative efforts between unmanned aerial vehicles (UAV) and ground surveillance cameras to locate person individuals. Despite the progress made by recent works, the impact of the discrepancy between the two views is underestimated. This limits the overall person search performance when training the model in a view-agnostic way. To address this, we propose a view-aware decomposition and unification (VADU) framework for ground-to-aerial person search. Specifically, we decompose the person search model to learn view-oriented modules for image feature encoding and person proposal generation. The data sampling and retrieval feature learning are also composed to cope with the decomposed model. This decomposition improves both person detection and discriminative feature learning within each view. On top of the decomposition, we propose view-aware unification to produce unified cross-view person features. Cross-view prototypical contrastive learning is introduced to enhance the unification between different views, enhancing model robustness to retrieve a target person in cameras of a different view. As the decomposed parts of the model are deployed on different devices for inference, this overall framework adds no extra computation cost in real-world applications. Extensive experiments demonstrate that the proposed method achieves superior person search performance and guarantees the efficiency of inference. The source code is available at https://github.com/QFWang-11/vadu.