About
TrapTagger is a powerful, free web application built by WildEye Conservation that leverages cutting-edge artificial intelligence to automate the analysis of camera-trap data. Designed for wildlife researchers and conservationists, it processes large volumes of images and videos to detect animals, classify species, and identify individual animals based on their unique coat patterns. The platform uses a three-stage AI pipeline: first, MegaDetector — an industry-standard open-source model — locates animals, vehicles, and humans while filtering out empty frames caused by wind and other disturbances; next, a species classification model identifies each detected animal; and finally, a coat-pattern similarity algorithm enables individual animal re-identification across datasets. To ensure data quality, TrapTagger only auto-labels images when confidence is high, routing uncertain cases to optimized manual annotation workflows that can be handled by large teams working in parallel. This hybrid AI-human approach minimizes labeling errors while maximizing throughput. As a cloud-based solution, TrapTagger securely manages and stores large quantities of camera-trap data and is accessible from anywhere. Annotated results are exportable as CSV or spreadsheet files, and the platform includes a suite of built-in analysis tools for ecological insight. TrapTagger is completely free, making professional-grade wildlife monitoring accessible to conservation teams and researchers of all sizes worldwide.
Key Features
- AI Animal Detection: Uses MegaDetector to automatically locate animals, vehicles, and humans in images and videos, filtering out empty files triggered by wind or environmental disturbances.
- Species Classification: A dedicated AI model classifies the species of each detected animal, enabling rapid identification across thousands of images without manual review.
- Individual Animal Identification: Calculates coat-pattern similarity between animals of the same species to build and maintain a database of known individuals over time.
- Hybrid Human-AI Annotation Workflows: Routes low-confidence detections to optimized manual annotation workflows, allowing large teams to work in parallel for accurate, quality-controlled results.
- Data Export & Built-In Analysis Tools: Exports annotated results as CSV or spreadsheets and includes built-in analysis tools to extract ecological insights from wildlife datasets.
Use Cases
- Conducting large-scale wildlife population surveys using motion-activated camera traps in national parks and nature reserves
- Tracking and re-identifying individual animals across datasets using AI-powered coat-pattern recognition for long-term behavioral or population studies
- Filtering thousands of empty or irrelevant camera-trap frames triggered by wind, vegetation, or vehicles to isolate genuine wildlife detections
- Running collaborative annotation projects where research teams or volunteers review uncertain AI detections in parallel to build clean, verified datasets
- Generating structured species detection records for biodiversity assessments, ecological research, and conservation planning reports
Pros
- Completely Free: TrapTagger is 100% free to use, removing financial barriers for conservation teams, NGOs, and independent researchers worldwide.
- Drastically Reduces Manual Work: Automates the most time-consuming parts of camera-trap data processing, freeing researchers to focus on analysis and conservation outcomes instead of sorting images.
- Cloud-Based & Secure: Manages and stores large volumes of camera-trap data securely in the cloud, accessible from anywhere without requiring local infrastructure.
- High-Quality Data with Confidence Thresholds: Only auto-labels data when the AI is highly confident, routing ambiguous detections to human review to maintain data integrity and minimize errors.
Cons
- Requires Internet Connectivity: As a cloud-based web application, TrapTagger requires an active internet connection, which may be a challenge in remote or low-connectivity field locations.
- Some Manual Annotation Still Required: Low-confidence or ambiguous images must be manually reviewed, meaning researchers cannot rely entirely on automated processing for full datasets.
- Occasional False Detections: MegaDetector can sometimes misidentify objects like rocks, vegetation, or man-made structures as animals, potentially skewing counts if not caught during review.
Frequently Asked Questions
Is TrapTagger really free to use?
Yes, TrapTagger is completely free. WildEye Conservation built it to lower barriers for wildlife researchers and conservationists globally, with no subscription or usage fees.
What file types does TrapTagger support?
TrapTagger supports both images and videos from camera traps. It processes them through AI pipelines to detect animals, classify species, and identify individuals by coat patterns.
How does individual animal identification work?
TrapTagger's third AI model analyzes the coat patterns of detected animals of the same species and calculates similarity scores to match individuals across images, enabling researchers to build a database of known animals over time.
What happens when the AI is uncertain about a detection?
When confidence falls below a set threshold, those images or videos are flagged for manual human annotation through optimized workflows that support large teams working in parallel, ensuring accuracy without sacrificing speed.
How do I access my results after processing?
Annotated data can be downloaded as a CSV or spreadsheet, including species counts, detection locations, dates, and timestamps, ready for use in your research and reporting workflows.
