DroneVis: Versatile Computer Vision Library for Drones
Ahmed Heakl, Fatma Youssef, Victor Parque, Walid Gomaa
TL;DR
DroneVis is a versatile Python library that simplifies the implementation and customization of computer vision algorithms on Parrot drones, supporting various tasks with comprehensive documentation and models.
Contribution
The paper presents DroneVis, a new, flexible library that integrates multiple computer vision tasks and models specifically for drone applications, with high-quality code and extensive documentation.
Findings
Supports diverse computer vision tasks on drones
Provides customizable models and features
Includes comprehensive usage documentation
Abstract
This paper introduces DroneVis, a novel library designed to automate computer vision algorithms on Parrot drones. DroneVis offers a versatile set of features and provides a diverse range of computer vision tasks along with a variety of models to choose from. Implemented in Python, the library adheres to high-quality code standards, facilitating effortless customization and feature expansion according to user requirements. In addition, comprehensive documentation is provided, encompassing usage guidelines and illustrative use cases. Our documentation, code, and examples are available in https://github.com/ahmedheakl/drone-vis.
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Video Surveillance and Tracking Methods · Robotics and Sensor-Based Localization
MethodsSparse Evolutionary Training · Sigmoid Activation · Tanh Activation · Attention Is All You Need · Long Short-Term Memory · Softmax · Lib · Linear Layer · Parrot