I Spy Through a Camera’s Eye: Divii in the Gwich’in Settlement Area

Goward, Sydney

doi:10.14430/arctic76639

ARCTIC

2022

DOI: 10.14430/arctic76639

|View full text |Cite

I Spy Through a Camera’s Eye: Divii in the Gwich’in Settlement Area

Sydney Goward¹

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

To crop or not to crop: Comparing whole‐image and cropped classification on a large dataset of camera trap images

Gadot,

Istrate,

Kim

et al. 2024

IET Computer Vision

View full text Add to dashboard Cite

Camera traps facilitate non‐invasive wildlife monitoring, but their widespread adoption has created a data processing bottleneck: a camera trap survey can create millions of images, and the labour required to review those images strains the resources of conservation organisations. AI is a promising approach for accelerating image review, but AI tools for camera trap data are imperfect; in particular, classifying small animals remains difficult, and accuracy falls off outside the ecosystems in which a model was trained. It has been proposed that incorporating an object detector into an image analysis pipeline may help address these challenges, but the benefit of object detection has not been systematically evaluated in the literature. In this work, the authors assess the hypothesis that classifying animals cropped from camera trap images using a species‐agnostic detector yields better accuracy than classifying whole images. We find that incorporating an object detection stage into an image classification pipeline yields a macro‐average F1 improvement of around 25% on a large, long‐tailed dataset; this improvement is reproducible on a large public dataset and a smaller public benchmark dataset. The authors describe a classification architecture that performs well for both whole and detector‐cropped images, and demonstrate that this architecture yields state‐of‐the‐art benchmark accuracy.

show abstract

To crop or not to crop: Comparing whole‐image and cropped classification on a large dataset of camera trap images

Gadot,

Istrate,

Kim

et al. 2024

IET Computer Vision

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

I Spy Through a Camera’s Eye: Divii in the Gwich’in Settlement Area

Cited by 1 publication

References 23 publications

To crop or not to crop: Comparing whole‐image and cropped classification on a large dataset of camera trap images

To crop or not to crop: Comparing whole‐image and cropped classification on a large dataset of camera trap images

Contact Info

Product

Resources

About