Learning to generalize aerial person re‐identification using the meta‐transfer method

Abstract

Person re-identification (Re-ID) aims to retrieve a person of interest across multiple nonoverlapping cameras. In recent years, to enable person Re-ID technology to play out its application value in real-world scenarios, visual surveillance through unmanned aerial vehicle (UAV) platforms has received intense attention, and aerial person datasets have been constructed. However, the pedestrian images captured by ground cameras and those captured by UAVs exhibit great differences. Person Re-ID methods based on ground person images have difficulty performing Re-ID on aerial person images. In this paper, we first use a meta-transfer method to learn to gener-aerial imagery, meta-transfer learning, person re-identification

Parameter instance learning with enhanced vision transformers for aerial person re‐identification

“…weaker appearance features than their ground counterparts, intensifying the difficulty of identification. Existing state-of-the-arts (SOTA) research in aerial person Re-ID 1,[7][8][9][10][11] predominantly relies on traditional Re-ID methods, constructing models based on local features, 1,12,13 which struggle to effectively address the specific challenges of aerial person Re-ID tasks.…”