Deep learning based person search in aerial imagery
The increasing availability of drones and their flexible employment for surveillance tasks will lead to large amounts of aerial video data in the near future. Similar to camera network data, such large data volumes will pose a challenge when fast analysis of the data is required, for example after a security incident. Key automated tasks that can help make the data more easily navigable are detection and re-identification of persons. While both tasks pose a challenge in themselves, the combination of both, often called person search, can often be of greatest benefit to analysts. In this work we address the task of person search in aerial images on the newly available P-DESTRE dataset. In particular our work aims at investigating the suitability of existing methods for person detection and re-identification in the aerial domain and taking a look at how top performing methods can be combined to realize an aerial person search system. Besides evaluation of the individual components we focus on analyzing the interplay between the detection and re-identification methods. In particular, we look at wether errors from the detection stage, such as misaligned detections or false positive detection, strongly affect the re-identification accuracies.