Computer vision enables in-situ monitoring of animal populations at a lower cost and with less ecosystem disturbance than with human observers. However, computer vision uncertainty may not be fully understood by end-users, and the uncertainty assessments performed by technology experts may not fully address end-user needs. This knowledge gap can yield misinterpretations of computer vision data, and trust issues impeding the transfer of valuable technologies. We bridge this gap with a user-centered analysis of the uncertainty issues. Key uncertainty factors, and their interactions, are identified from the perspective of a core task in ecology research and beyond: counting individuals from different classes. We highlight factors for which uncertainty assessment methods are currently unavailable. The remaining uncertainty assessment methods are not interoperable. Hence it is currently difficult to assess the combined results of multiple uncertainty factors, and their impact on end-user counting tasks. We propose a framework for assessing the multifactorial uncertainty propagation along the data processing pipeline. It integrates methods from both computer vision and ecology domains, and aims at supporting the statistical analysis of abundance trends for population monitoring. Our typology of uncertainty factors and our assessment methods were drawn from interviews with marine ecology and computer vision experts, and from prior work for a fish monitoring application. Our findings contribute to enabling scientific research based on computer vision.
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International Conference on Data Science and Advanced Analytics
Human-Centered Data Analytics

Beauxis-Aussalet, E., & Hardman, L. (2015). Multifactorial Uncertainty Assessment for Monitoring Population Abundance using Computer Vision. In Proceedings of International Conference on Data Science and Advanced Analytics 2015 (DSAA) (pp. 1–10). IEEE. doi:10.1109/DSAA.2015.7344879