SUPR
Computer vision/deep learning model to classifiy age and sex of ungulates species from camera trap images in Sweden
Dnr:

NAISS 2023/23-610

Type:

NAISS Small Storage

Principal Investigator:

Magali Frauendorf

Affiliation:

Sveriges lantbruksuniversitet

Start Date:

2023-11-27

End Date:

2024-10-01

Primary Classification:

40502: Fish and Wildlife Management

Webpage:

Allocation

Abstract

I am involved in the Viltbild program, where SLU collaborates with the Swedish Association for Hunting and Wildlife Management. With Viltbild, we develop a platform to collect and organise camera-trapping images taken by hunters all over Sweden. On a long-term, this can serve as a digital monitoring tool to monitor Swedish game species with the help of hunters. For this project I work on building algorithms that can distinguish sex and age (juveniles, adults) on camera trap images from five ungulate species (occurring in Sweden). An automatic identification of sex and age can help us to estimate reproductive performance and in turn result in a quicker feedback loop between monitoring and management, leading to management that is more appropriate.