Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning.

ABSTRACT: Using machine learning (ML) to automate camera trap (CT) image processing is advantageous for time-sensitive applications. However, little is currently known about the factors influencing such processing. Here, we evaluate the influence of occlusion, distance, vegetation type, size class, height, subject orientation towards the CT, species, time-of-day, colour, and analyst performance on wildlife/human detection and classification in CT images from western Tanzania. Additionally, we compared the detection and classification performance of analyst and ML approaches. We obtained wildlife data through pre-existing CT images and human data using voluntary participants for CT experiments. We evaluated the analyst and ML approaches at the detection and classification level. Factors such as dista

SUBMITTER: Westworth SOA 

PROVIDER: S-EPMC9319727 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature