English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Drones are conventionally controlled using joysticks, remote controllers,mobile applications, and embedded computers. A few significant issues withthese approaches are that drone control is limited by the range ofelectromagnetic radiation and susceptible to interference noise. In this studywe propose the use of hand gestures as a method to control drones. Weinvestigate the use of computer vision methods to develop an intuitive way ofagent-less communication between a drone and its operator. Computervision-based methods rely on the ability of a drone's camera to capturesurrounding images and use pattern recognition to translate images tomeaningful and/or actionable information. The proposed framework involves a fewkey parts toward an ultimate action to be taken. They are: image segregationfrom the video streams of front camera, creating a robust and reliable imagerecognition based on segregated images, and finally conversion of classifiedgestures into actionable drone movement, such as takeoff, landing, hovering andso forth. A set of five gestures are studied in this work. Haar feature-basedAdaBoost classifier is employed for gesture recognition. We also envisagesafety of the operator and drone's action calculating the distance based oncomputer vision for this task. A series of experiments are conducted to measuregesture recognition accuracies considering the major scene variabilities,illumination, background, and distance. Classification accuracies show thatwell-lit, clear background, and within 3 ft gestures are recognized correctlyover 90%. Limitations of current framework and feasible solutions for bettergesture recognition are discussed, too. The software library we developed, andhand gesture data sets are open-sourced at project website.

Hand Gesture Controlled Drones: An Open Source Library