Pre-Processing Layer

Once a submission clears the Data Ingestion Layer, it moves into the Pre-Processing Layer, where the raw video is refined, cleaned, and anonymized. If ingestion serves as the gatekeeper, pre-processing acts as the polishing stage, ensuring that every video entering the annotation pipeline is consistent, privacy-safe, and free from technical artifacts that could compromise downstream analysis.

The first responsibility of this layer is to run deeper video integrity and quality checks. While ingestion verifies that frame rates, resolution, and duration fall within acceptable thresholds, pre-processing evaluates the content itself for usability. Videos are scanned for corruption at the frame level, with broken or incomplete frames automatically repaired or removed. Blurriness is measured and normalized, ensuring that submissions retain sufficient clarity to be interpretable by both humans and AI models. Similarly, a motion score is applied to confirm that the clip reflects natural egocentric movement rather than static or artificial footage. Videos that are too blurry, too static, or otherwise unsuitable are flagged for rejection.

Beyond quality validation, this layer enforces privacy preservation. Sensitive elements are automatically detected and anonymized, including faces, license plates, passports, credit cards, and corporate logos. Dynamic blurring and masking techniques are applied consistently across the dataset, ensuring that no personally identifiable information or brand-sensitive details survive into later stages of processing. Even if these elements were overlooked at ingestion, pre-processing provides a second pass of defense, guaranteeing that videos are fully anonymized before any annotation occurs.

Another key function of the Pre-Processing Layer is standardization. Videos are normalized into a common frame structure so that downstream annotation models can operate uniformly. For example, all tasks are segmented into a fixed number of temporal windows, with clear boundaries marking the beginning and end of the action. This eliminates inconsistencies caused by user-specific recording habits, creating a predictable input structure for models that need to detect objects, actions, and task sequences.

Importantly, pre-processing is where Orn begins to enforce resilience against manipulation. Users who attempt to bypass liveness gestures, splice together unrelated clips, or recycle external content are more likely to be caught here, since the system evaluates temporal coherence, continuity, and contextual cues at a finer level of detail than during ingestion. Each time a new type of manipulation is detected, the system retrains its classifiers, gradually strengthening its defenses against future attempts.

By the time a video leaves the Pre-Processing Layer, it has been transformed into a consistent, high-quality, and privacy-protected submission. Only at this stage are videos committed to permanent storage, ensuring that all retained data is fully anonymized and compliant with privacy requirements. It is stripped of sensitive details, standardized into uniform segments, and validated for both visual clarity and natural egocentric motion. This stage acts as the final preparation step before annotation, ensuring that the content moving forward is not just authentic, but also clean, safe, and structured for analysis.

All thresholds, parameters, and detection methods described are subject to continuous refinement as technology advances and as the requirements of the ecosystem evolve.