Összes szerző

Ahmad Aamir

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Berekméri Evelin
Investigation of a red-footed falcon hub in Angola with deep learning

Aug 29 - kedd

15:30 – 17:00

I. Poszterszekció

P04

Investigation of a red-footed falcon hub in Angola with deep learning

Evelin Berekméri^1,2, Nico Klar,^3,4, Eric Price^4,5, Péter Palatitz⁶, Aamir Ahmad^4,5, Máté Nagy^1,2,7

¹Department of Biological Physics, Eötvös Loránd University, Budapest, Hungary

²MTA-ELTE Lendület Collective Behaviour Research Group, Hungarian Academy of Sciences, Budapest, Hungary

³Center for Solar Energy and Hydrogen Research Baden-Württemberg, Germany

⁴Institute of Flight Mechanics and Controls, University of Stuttgart, Stuttgart, Germany

⁵Max Planck Institute for Intelligent Systems, Tübingen, Germany

⁶MME/BirdLife Hungary, Budapest, Hungary

⁷Max-Planck Institute of Animal Behavior, Konstanz, Germany

Automating the perception of our environment with object detection has an increasing number of applications, not only in everyday life, such as for self-driving cars or in security, but also in scientific research. Object detection is a deep learning-based computer vision technique that involves recognising predefined classes of objects in visual recordings and locating them within the frame of the recording.

In this study, we focus on a recently discovered, yet unpublished migratory bird hub in Angola, where red-footed falcons gather from different points of the world. These hubs play a crucial role in connecting remote locations worldwide, potentially assisting in disease transmission, and acting as indicators of global environmental changes due to the birds' sensitivity to environmental variations.

Our aim is to identify and track birds on video recordings from the hub providing insight into their population size and preparing further collective behaviour studies such as identifying when they are making use of thermals to gain altitude during their flights. We utilise state-of-the-art object detection frameworks and utilize the latest developments in deep learning and computer vision, taking into consideration that we also aim to deploy our method for real-time, on-board detection on drones.

Currently our understanding of the population size of this species, based on traditional counting methods, has multiple orders of magnitude uncertainty. Our approach offers numerous advantages over traditional procedures, being not only faster and more cost-efficient but also can be more accurate and it also offers real-time monitoring. Such approaches have been used to study the population of other species, such as bat colonies.

Acknowledgment

PREPARED WITH THE PROFESSIONAL SUPPORT OF THE DOCTORAL STUDENT SCHOLARSHIP PROGRAM OF THE CO-OPERATIVE DOCTORAL PROGRAM OF THE MINISTRY OF INNOVATION AND TECHNOLOGY FINANCED FROM THE NATIONAL RESEARCH, DEVELOPMENT AND INNOVATION FUND.

E.B. acknowledges MTA-ELTE Lendület Collective Behaviour Research Group for financial suppport.

This project was partially supported by the National Research, Development and Innovation Office under grant no. K128780.