A robust metric distance and height estimation pipeline for wildlife camera trap imagery

Obtaining accurate metric measurements from 2D images captured by camera traps is valuable for ecological research, particularly population density estimation and morphometric analyses. Traditional methods require extensive manual calibration and image processing, making large-scale studies impractical. In this research study, we present a robust and automated pipeline that integrates monocular depth estimation (MDE) models with automated calibration and animal detection methods. Our pipeline uses deep learning based MDEs, including Dense Prediction Transformers (DPT), MiDaS 3.1, DepthAnything, DepthPro and UniDepthV2, along with multiple calibration methods such as linear, polynomial, and piecewise regression, to improve distance estimation accuracy. We introduce an automated mask generation and cross-camera calibration process to reduce manual effort. The proposed pipeline was validated using controlled ground truth data collected in Oxfordshire, UK, and subsequently tested on real-world wildlife data from the Bubye Valley Conservancy, Zimbabwe. Our proposed technique using UniDepthV2 MDE and piecewise linear regression for calibration reduces overall distance estimation error by up to 35% compared to existing approaches. Furthermore, we implemented height estimation as an indirect validation mechanism, presenting a correlation between true and estimated heights for different animals. Our fully automated pipeline advances camera trap-based wildlife monitoring, reducing human effort while improving scalability and reliability for conservation research. • Automated pipeline for metric distance and height estimation • Advanced calibration techniques including linear and non-linear models • Multiple calibration approaches with different MDEs • Test of cross-camera calibration in different environments • Height estimation as validation for distance estimation and morphometrics