Investigating Open-World Person Re-identification Using a Drone

Layne, Ryan; Hospedales, Timothy M.; Gong, Shaogang

doi:10.1007/978-3-319-16199-0_16

Cited by 26 publications

(27 citation statements)

References 32 publications

(52 reference statements)

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“…Finally, whilst the aim of the current study was to examine person identification from drone-captured footage by human observers, similar studies in computer vision are now beginning to emerge 57 . This raises the question of how the accuracy of human observers and machine algorithms in person identification might compare.…”

Section: Discussionmentioning

confidence: 99%

Person identification from aerial footage by a remote-controlled drone

Bindemann

Fysh

Sage

et al. 2017

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 99%

Person identification from aerial footage by a remote-controlled drone

Bindemann

Fysh

Sage

et al. 2017

Sci Rep

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“…We examined the following UAV datasets: UCF's dataset (http://crcv.ucf.edu/data/UCF_Aerial_ Action.php), VIRAT [51], MRP [52], the privacy-based mini-drones dataset [53], the aerial videos dataset described in [54], UAV123 [55], DTB70 [57], Okutama-Action [58], VisDrone [64], CARPK [59], SEAGULL [60], DroneFace [61], and the aerial video dataset described in [56]). A total of 43 videos (RGB, 30 frame per second (fps), 1280 × 720 or 720 × 480) were selected from databases VIRAT, UAV123, and DTB70.…”

Section: Content Selectionmentioning

confidence: 99%

“…While it is now rather easy to find eye tracking data on typical images [35,[37][38][39][40][41][42][43][44][45] or videos [46][47][48][49][50], and that there are many UAV content datasets [7,[51][52][53][54][55][56][57][58][59][60][61][62], it turns out to be extremely difficult to find eye-tracking data on UAV content. This is even truer when we consider dynamic salience, which refers to salience for video content.…”

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confidence: 99%

EyeTrackUAV2: A Large-Scale Binocular Eye-Tracking Dataset for UAV Videos

Perrin

Krassanakis

Zhang

et al. 2020

Drones

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The fast and tremendous evolution of the unmanned aerial vehicle (UAV) imagery gives place to the multiplication of applications in various fields such as military and civilian surveillance, delivery services, and wildlife monitoring. Combining UAV imagery with study of dynamic salience further extends the number of future applications. Indeed, considerations of visual attention open the door to new avenues in a number of scientific fields such as compression, retargeting, and decision-making tools. To conduct saliency studies, we identified the need for new large-scale eye-tracking datasets for visual salience in UAV content. Therefore, we address this need by introducing the dataset EyeTrackUAV2. It consists of the collection of precise binocular gaze information (1000 Hz) over 43 videos (RGB, 30 fps, 1280 × 720 or 720 × 480). Thirty participants observed stimuli under both free viewing and task conditions. Fixations and saccades were then computed with the dispersion-threshold identification (I-DT) algorithm, while gaze density maps were calculated by filtering eye positions with a Gaussian kernel. An analysis of collected gaze positions provides recommendations for visual salience ground-truth generation. It also sheds light upon variations of saliency biases in UAV videos when opposed to conventional content, especially regarding the center bias.

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“…One potential solution to this problem might be the development of person-recognition algorithms. Recent work has made progress in developing systems that are capable of tracking [51], detecting [52] and identifying individuals in drone footage [53]. In addition, automated recognition systems have demonstrated near-perfect performance in some benchmark tests [54,55].…”

Section: Possible Solutionsmentioning

confidence: 99%

Person Identification from Drones by Humans: Insights from Cognitive Psychology

Fysh

Bindemann

2018

Drones

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The deployment of unmanned aerial vehicles (i.e., drones) in military and police operations implies that drones can provide footage that is of sufficient quality to enable the recognition of strategic targets, criminal suspects, and missing persons. On the contrary, evidence from Cognitive Psychology suggests that such identity judgements by humans are already difficult under ideal conditions, and are even more challenging with drone surveillance footage. In this review, we outline the psychological literature on person identification for readers who are interested in the real-world application of drones. We specifically focus on factors that are likely to affect identification performance from drone-recorded footage, such as image quality, and additional person-related information from the body and gait. Based on this work, we suggest that person identification from drones is likely to be very challenging indeed, and that performance in laboratory settings is still very likely to underestimate the difficulty of this task in real-world settings.

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Investigating Open-World Person Re-identification Using a Drone

Cited by 26 publications

References 32 publications

Person identification from aerial footage by a remote-controlled drone

Person identification from aerial footage by a remote-controlled drone

EyeTrackUAV2: A Large-Scale Binocular Eye-Tracking Dataset for UAV Videos

Person Identification from Drones by Humans: Insights from Cognitive Psychology

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