Proceedings ArticleDOI
DroneSURF: Benchmark Dataset for Drone-based Face Recognition
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TLDR
This research presents a novel large-scale drone dataset, DroneSURF: Drone Surveillance of Faces, in order to facilitate research for face recognition, along with information regarding the data distribution, protocols for evaluation, and baseline results.Abstract:
Unmanned Aerial Vehicles (UAVs) or drones are often used to reach remote areas or regions which are inaccessible to humans. Equipped with a large field of view, compact size, and remote control abilities, drones are deemed suitable for monitoring crowded or disaster-hit areas, and performing aerial surveillance. While research has focused on area monitoring, object detection and tracking, limited attention has been given to person identification, especially face recognition, using drones. This research presents a novel large-scale drone dataset, DroneSURF: Drone Surveillance of Faces, in order to facilitate research for face recognition. The dataset contains 200 videos of 58 subjects, captured across 411K frames, having over 786K face annotations. The proposed dataset demonstrates variations across two surveillance use cases: (i) active and (ii) passive, two locations, and two acquisition times. DroneSURF encapsulates challenges due to the effect of motion, variations in pose, illumination, background, altitude, and resolution, especially due to the large and varying distance between the drone and the subjects. This research presents a detailed description of the proposed DroneSURF dataset, along with information regarding the data distribution, protocols for evaluation, and baseline results.read more
Citations
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Face recognition in drone-shot videos
TL;DR: This research presents DroneSURF dataset, a novel large-scale drone videos dataset, collected over two segments, intended to facilitate research for drone-based face detection and recognition.
Proceedings ArticleDOI
Hierarchically Organized Computer Vision in Support of Multi-Faceted Search for Missing Persons
TL;DR: In this paper , the authors propose AirSight, a model that hierarchically combines multiple Computer Vision (CV) models, exploits both onboard and off-board computing capabilities, and engages humans interactively in the search.
Journal ArticleDOI
UIT-ADrone: A Novel Drone Dataset for Traffic Anomaly Detection
TL;DR: The UIT-ADrone dataset as mentioned in this paper contains 51 videos with total data traffic of nearly 6.5 h, captured across 206K frames with ten abnormal event types. And the authors comprehensively evaluate the current state-of-the-art algorithms and what anomaly detection can do in drone-based video surveillance.
CoNAN: Conditional Neural Aggregation Network For Unconstrained Face Feature Fusion
TL;DR: In this article , a feature distribution conditioning approach called CoNAN is proposed for template aggregation, which aims to learn a context vector conditioned over the distribution information of the incoming feature set, which is utilized to weigh the features based on their estimated informativeness.
Journal ArticleDOI
Archangel: A Hybrid UAV-based Human Detection Benchmark with Position and Pose Metadata
Yingzhe Shen,Yaesop Lee,Heesung Kwon,Damon M. Conover,S. Bhattacharya,Nikolas Vale,Joshua D. Gray,G. J. Leong,Kenneth D. Evensen,Frank E. Skirlo +9 more
TL;DR: In this paper , the first UAV-based object detection dataset composed of real and synthetic subsets captured with similar imagining conditions and UAV position and object pose metadata is presented. And the benefits of leveraging the metadata during model evaluation are discussed.
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