End-to-end workflow for privacy-preserving video de-identification and quality assurance in behavioral research.
Mask it with MaskAnything. Verify it with MaskBench. Archive it FAIRly.
Video data is central to behavioral research across psychology, communication science, and human-computer interaction. Yet privacy concerns create two interconnected operational challenges that limit data sharing and reproducibility.
How do you remove identifying information from video while preserving the behavioral signals your research depends on?
How do you verify that your de-identification hasn't compromised downstream analyses like pose estimation?
MaskingOPS integrates de-identification, quality assurance, and data archiving into a single operational workflow.
Upload your video, select a masking strategy (blur, pixelation, solid fill, contour, or skeleton), and let SAM2-based segmentation automatically detect and track people across frames. Use the human-in-the-loop editor to refine any tracking errors.
Run your raw and masked videos through multiple pose estimators. MaskBench computes standardized accuracy metrics (PCK, RMSE) and kinematic smoothness metrics (velocity, acceleration, jerk) to quantify the impact of masking on pose estimation quality.
Review quality reports. If pose estimation accuracy falls below your threshold, adjust your masking strategy and repeat. The iterative loop ensures you find the optimal balance between privacy protection and research utility for your specific use case.
Export masked videos with full provenance metadata: what was masked, which strategy, processing parameters, and quality scores. Deposit to DANS, institutional repositories, or other FAIR-compliant archives with confidence that your data is both privacy-safe and research-ready.
Our benchmarking shows that the choice of masking strategy dramatically affects downstream analysis quality. Not all masking is equal.
Pose information preserved with Gaussian blur
Pose information preserved with pixelation
Pose information preserved with solid fill
| Strategy | Privacy Level | Pose Quality | Best For |
|---|---|---|---|
| Gaussian Blur | Moderate |
86%
|
Gesture & movement research |
| Contour | Moderate-High |
~70%
|
Spatial interaction studies |
| Pixelation | High |
~52%
|
Moderate privacy needs |
| Skeleton Overlay | Very High |
~45%
|
Maximum privacy with motion |
| Solid Fill | Maximum |
~31%
|
Clinical / highly sensitive |
De-identification framework
Flexible video de-identification built on the Segment Anything Model 2 (SAM2). Supports multiple masking strategies, multi-person tracking, consent-aware processing, and human-in-the-loop refinement through an interactive web editor.
Benchmarking framework
Open-source benchmarking framework for evaluating pose estimation quality on raw and masked videos. Integrates multiple pose estimators with standardized accuracy and kinematic metrics for systematic quality assurance.
Different research scenarios call for different privacy-utility trade-offs. Here are our recommended configurations.
Full-body kinematics must remain intact. Blur preserves the most pose information while providing moderate privacy.
Facial features must be fully obscured while preserving body orientation and spatial relationships.
Processing entire corpora for long-term storage. Use default presets with batch processing for consistency.
Maximum privacy with human-in-the-loop verification. Every frame must be checked for residual identifiability.
MaskingOPS outputs are designed for integration with research data management workflows. Every masked dataset includes provenance documentation, processing metadata, and quality assurance reports.
Persistent identifiers and rich metadata for masked datasets
Compatible with DANS, ODISSEI, and institutional repositories
Standard output formats (JSON, CSV) and documented schemas
Clear licenses, processing provenance, quality scores attached
MaskingOPS helps researchers meet GDPR Article 89 requirements for processing personal data for scientific research purposes. The workflow produces documented evidence of data minimization and proportionate privacy measures.
Masking is not a substitute for informed consent. MaskingOPS supports consent-aware processing where different participants can receive different masking levels based on their consent status.
Step-by-step guide submitted to Behavior Research Methods, covering the full MaskingOPS pipeline with worked examples.
Under review — Behavior Research MethodsWatch the complete workflow from video upload through quality verification to FAIR archiving.
Watch demo →Hands-on training events where researchers learn MaskingOPS with their own data and expert guidance.
View schedule →MaskingOPS is free, open source, and designed for researchers. Deploy with Docker, process your first video, and verify the results.