Unsupervised Person Re-Identification in Aerial Imagery
Khadija Khaldi, Vuong D. Nguyen, Pranav Mantini, Shishir K. Shah
Abstract
The rapidly increasing use of unmanned aerial vehicles (UAVs) for surveillance has paved the way for advanced image analysis techniques to enhance public safety. Among many others, person re-identification (ReID) is a key task. However, much of the current literature is centered on research datasets, often overlooking the practical challenges and unique requirements of UAV-based aerial datasets. We close this gap by analyzing these challenges, such as view-point variations and lack of annotations, and proposing a framework for aerial person re-identification under unsupervised setting. Our framework integrates three stages: generative, contrastive, and clustering, designed to extract view-invariant features for ReID without the need for labels. Finally, we provide a detailed quantitative and qualitative analysis on two UAV-based ReID datasets, and demonstrate that our proposed model outperforms state-of-the-art methods with an improvement of up to 2% in rank-1 scores.