GreenCrossingAI: A Camera Trap/Computer Vision Pipeline for Environmental Science Research Groups

TL;DR

GreenCrossingAI presents an accessible, low-resource computer vision pipeline tailored for small environmental research groups to efficiently process and analyze camera trap data using ML/AI tools.

Contribution

The paper introduces a practical, on-premise pipeline specifically designed for resource-limited research groups to facilitate camera trap data analysis with ML/AI integration.

Findings

Enables processing of large camera trap datasets on limited hardware

Provides a user-friendly workflow for data annotation and inference

Improves data analysis efficiency for small research teams

Abstract

Camera traps have long been used by wildlife researchers to monitor and study animal behavior, population dynamics, habitat use, and species diversity in a non-invasive and efficient manner. While data collection from the field has increased with new tools and capabilities, methods to develop, process, and manage the data, especially the adoption of ML/AI tools, remain challenging. These challenges include the sheer volume of data generated, the need for accurate labeling and annotation, variability in environmental conditions affecting data quality, and the integration of ML/AI tools into existing workflows that often require domain-specific customization and computational resources. This paper provides a guide to a low-resource pipeline to process camera trap data on-premise, incorporating ML/AI capabilities tailored for small research groups with limited resources and computational expertise. By focusing on practical solutions, the pipeline offers accessible approaches for data transmission, inference, and evaluation, enabling researchers to discover meaningful insights from their ever-increasing camera trap datasets.