DroneSURF: Benchmark Dataset for Drone-based Face Recognition

Kalra, Isha; Singh, Maneet; Nagpal, Shruti; Singh, Richa; Vatsa, Mayank; Sujit, P. B.

doi:10.1109/fg.2019.8756593

Cited by 54 publications

(18 citation statements)

References 21 publications

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“…This phase aims to establish enhanced wireless communication links between a drone and its ground station by optimizing link budget parameters. This can be done by tuning the input parameters of drone altitude (h t ), and elevation angle (θ ), which reportedly affect wireless connectivity in any space-based communication system [32,53]. To carry out the optimization, the RBF-NN tool is used as it supports data selection, network creation and training, and network performance evaluation using MSE and regression analysis.…”

Section: Phase 1: Optimizing Link Budget Parametersmentioning

confidence: 99%

“…During selection of the best optimized value, the network adaptively fine-tunes the free system parameters based on the corrections which minimize the MSE between inputs y i and the desired output d i , which represents the parameter bounds that are considered will improve channel performance. RBF-NN neurons compete at every iteration until either there are no further centre updates, or the maximum number of iterations is reached [32,53,54].…”

Section: Pl[db] 40 Log(d) − [10 Log G(h T ) + 10 Log G(h R ) + 20 Log(h T ) + 20 Log(hmentioning

confidence: 99%

“…The authors argue that on-line decision making for optimization would improve drone's functionality. The work in [32] highlights some common issues that relate to drone face surveillance, including the effect of motion, differences in pose, illumination, a drone's altitude, and a camera's resolution. These effects have a negative relationship with distance between the drone and ground subjects.…”

Section: Introductionmentioning

confidence: 99%

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Coupling multifunction drones with AI in the fight against the coronavirus pandemic

2021

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When COVID-19 was declared as a pandemic by the World Health Organization on 11 March 2020, national governments and health authorities across the world begun considering different preventive measures to fight against the coronavirus outbreak. Researchers and tech companies worldwide have been striving to utilize advanced technologies to aid in the fight against the Covid-19 outbreak. This paper aims to couple multifunction drone with AI to deliver wireless services that will help the fight against the Coronavirus pandemic. The proposed drone-eye-system with its thermal imaging cameras and an AI framework utilizes a Convolutional Neural Network (CNN) with its Modified Artificial Neural Network (MANN) for face mask detection of people wearing masks in public. The system can perform basic diagnostic functions such as elevated body temperatures for helping minimize the risk of spreading the infection through close contact. The AI framework evolve an optimized elevation angle $$\uptheta $$ θ and altitude $${\mathrm{h}}_{\mathrm{t}}$$ h t to enhance wireless connectivity between a drone and a ground station, which in turn leads to better throughput and power consumption. The proposed framework has been developed using the MATLAB toolbox and shows promising results with an accuracy of face mask detection of 82.63%, with an F1-score of 0.98, and an enhanced by 10% link budget parameters.

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Section: Phase 1: Optimizing Link Budget Parametersmentioning

confidence: 99%

Section: Pl[db] 40 Log(d) − [10 Log G(h T ) + 10 Log G(h R ) + 20 Log(h T ) + 20 Log(hmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coupling multifunction drones with AI in the fight against the coronavirus pandemic

2021

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“…The first large-scale dataset of images collected from a UAV with the aim of testing face recognition performance is DroneSURF [ 124 ]. The dataset contains 200 videos of 58 different subjects, captured across 411,000 frames, with over 786,000 face annotations.…”

Section: Eye Level Viewmentioning

confidence: 99%

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

et al. 2020

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The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed.

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“…These datasets cover multiple research disciplines but mainly in the security, industrial, and agricultural sectors. Examples of such application-specific drone datasets include datasets for object detection [7,8], datasets for vehicle trajectory estimation [9,10], datasets for object tracking [11,12], datasets for human action recognition [13][14][15][16], datasets for gesture recognition [17][18][19], datasets for face recognition [20,21], a dataset for fault detection in photovoltaic plants [22], datasets for geographic information system [23,24], and datasets for agriculture [25,26].…”

Section: Introductionmentioning

confidence: 99%

Drone-Action: An Outdoor Recorded Drone Video Dataset for Action Recognition

Perera

Law

Chahl

2019

Drones

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Aerial human action recognition is an emerging topic in drone applications. Commercial drone platforms capable of detecting basic human actions such as hand gestures have been developed. However, a limited number of aerial video datasets are available to support increased research into aerial human action analysis. Most of the datasets are confined to indoor scenes or object tracking and many outdoor datasets do not have sufficient human body details to apply state-of-the-art machine learning techniques. To fill this gap and enable research in wider application areas, we present an action recognition dataset recorded in an outdoor setting. A free flying drone was used to record 13 dynamic human actions. The dataset contains 240 high-definition video clips consisting of 66,919 frames. All of the videos were recorded from low-altitude and at low speed to capture the maximum human pose details with relatively high resolution. This dataset should be useful to many research areas, including action recognition, surveillance, situational awareness, and gait analysis. To test the dataset, we evaluated the dataset with a pose-based convolutional neural network (P-CNN) and high-level pose feature (HLPF) descriptors. The overall baseline action recognition accuracy calculated using P-CNN was 75.92%.

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DroneSURF: Benchmark Dataset for Drone-based Face Recognition

Cited by 54 publications

References 21 publications

Coupling multifunction drones with AI in the fight against the coronavirus pandemic

Coupling multifunction drones with AI in the fight against the coronavirus pandemic

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

Drone-Action: An Outdoor Recorded Drone Video Dataset for Action Recognition

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