Automated methods for processing camera trap video data for distance sampling

Trevor Bak; Richard J. Camp; Matthew D. Burt; Scott Vogt

doi:10.1071/PC25008

RESEARCH ARTICLE (Open Access)

Previous Next Contents Vol 31(4)

Automated methods for processing camera trap video data for distance sampling

Trevor Bak

^A ^* , Richard J. Camp

^B , Matthew D. Burt ^C and Scott Vogt ^D

+ Author Affiliations

- Author Affiliations

^A Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo, P.O. Box 44, Hawaii National Park, HI 96718, USA.

^B US Geological Survey, Pacific Island Ecosystems Research Center, Kīlauea Field Station, P.O. Box 44, Hawaii National Park, HI 96718, USA.

^C Naval Facilities Engineering Systems Command Pacific, 258 Makalapa Drive Suite 100, Joint Base Pearl Harbor-Hickam, HI 96860, USA.

^D Public Works Division, Environmental Office, Yokosuka Navy Base, PSC 473 Box 3244, FPO AP 96349, Yokosuka, Japan.

^* Correspondence to: tbak@hawaii.edu

Handling Editor: Tim Doherty

Pacific Conservation Biology 31, PC25008 https://doi.org/10.1071/PC25008

Submitted: 8 February 2025 Accepted: 30 May 2025 Published: 23 June 2025

Abstract

Context

Population monitoring is an essential need for tracking biodiversity and judging efficacy of conservation management actions, both globally and in the Pacific. However, population monitoring efforts are often temporally inconsistent and limited to small scales. Motion-activated cameras (‘camera traps’) offer a way to cost-effectively monitor populations, but they also generate large amounts of data that are time intensive to process.

Aims

To develop an automated pipeline for processing videos of ungulates (Philippine deer, Rusa marianna; and pigs, Sus scrofa) on Andersen Air Force Base in Guam.

Methods

We processed camera videos with a machine learning model for object detection and classification. To estimate density using distance sampling methods, we used a separate machine learning model to estimate the distance of target animals from the camera. We compared density estimates generated using manual versus automated methods and assessed accuracy and processing time saved.

Key results

The object detection and classification model achieved an overall accuracy >80% and F1 score ≥0.9 and saved 36.9 h of processing time. The automated distance estimation was fairly accurate, with a 1.1 m (±1.4 m) difference from manual distance estimates, and saved 16.8 h of processing time. Density estimates did not differ substantially between manual and automated distance estimation.

Conclusions

Machine learning models accurately processed camera videos, allowing efficient estimates of density from camera data.

Implications

Further adoption of motion-activated cameras coupled with automated processing could lead to continuous, large-scale monitoring of populations, helping to understand and address changes in biodiversity.

Keywords: automated processing, biodiversity, camera traps, conservation management, distance sampling, Guam, machine learning, ungulates.

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