This document presents an early-stage research hypothesis comparing traditional motion-based detection methods with computer vision–based artificial intelligence approaches for wildlife camera trap systems. The hypothesis proposes that vision-based models trained on labeled wildlife imagery may reduce false detections and improve species-level classification accuracy when compared to passive infrared (PIR) and pixel-change motion triggers commonly used in trail cameras. The work outlines the underlying assumptions of each detection approach, defines anticipated performance differences, and establishes a conceptual framework to inform future experimental design and empirical evaluation. The hypothesis is intended to guide subsequent data collection, model training, and validation efforts using real-world camera trap imagery collected in rural and agricultural environments. This document is pre-publication and shared to invite technical and methodological feedback prior to formal peer-reviewed submission. No experimental results are claimed.
Trent Adams (Wed,) studied this question.