Abstract
Remote-controlled aerial drones (or unmanned aerial vehicles; UAVs) are employed for surveillance by the military and police, which suggests that drone-captured footage might provide sufficient information for person identification. This study demonstrates that person identification from drone-captured images is poor when targets are unfamiliar (Experiment 1), when targets are familiar and the number of possible identities is restricted by context (Experiment 2), and when moving footage is employed (Experiment 3). Person information such as sex, race and age is also difficult to access from drone-captured footage (Experiment 4). These findings suggest that such footage provides a particularly poor medium for person identification. This is likely to reflect the sub-optimal quality of such footage, which is subject to factors such as the height and velocity at which drones fly, viewing distance, unfavourable vantage points, and ambient conditions.
Highlights
Unmanned aerial vehicles (UAVs), commonly referred to as drones, are increasingly utilised by police and the military
A compelling body of research already exists on person identification in other applied settings, such as passport control[8,9], closed-circuit television (CCTV)[10,11], and eyewitness scenarios[12,13]
Observers’ ability to match drone-captured images to high-quality photographs of unfamiliar faces was at or below chance, with accuracy averaging 43% across camera conditions, which indicates that positive person identifications could not be made reliably
Summary
Unmanned aerial vehicles (UAVs), commonly referred to as drones, are increasingly utilised by police and the military. The current study reports four experiments that investigate this issue, by examining the accuracy of person identification from drone-captured footage of a football (soccer) match at a UK university This set up presents a natural scenario that should provide relatively favourable conditions for image capture and subsequent person recognition. This research demonstrates that familiar people, who are known to an observer, can be identified with good accuracy[14,15] This is found under challenging conditions, for example, when people are viewed in poor-quality surveillance footage[11], or heavily degraded video[16], or when they are only seen briefly[17], partially[18,19], or in unfavourable non-frontal views[20]. We investigate these questions across several tasks to examine the identification of unfamiliar (Experiment 1) and familiar people (Experiment 2 and 3), as well as the perception of a person’s sex, race, and age from drone-captured footage (Experiment 4)
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