We partnered with a US-based startup to engineer the backend video infrastructure for an innovative marijuana detection device. The goal was to automate roadside sobriety test workflows by replicating and enhancing traditional Drug Recognition Expert (DRE) methods using VR technology and real-time AI drug testing. Our solution supported secure video processing, audio/subtitle overlays, and seamless cloud integration to improve accuracy, traceability, and scalability. This advanced eye tracking sobriety test system is now actively used in law enforcement and workplace safety protocols to assess cannabis-induced impairment.
Manual DRE eye tests lacked objectivity, standardization, and reliable video documentation, creating inconsistencies in impairment assessments during roadside sobriety test procedures.
No solution existed to reliably capture, process, and store high-volume eye movement recordings, a requirement for scalable automated sobriety test deployment.
Raw recordings lacked guided voice prompts and synchronized subtitle overlays, critical for regulatory reviews, training consistency, and future rehabilitation app development.
High-sensitivity video data needed encryption, structured metadata tagging, and privacy controls, which were absent in existing video workflows.
No reliable way existed to connect recordings to session metadata like user ID, test type, and timestamps, hindering compliance and usability.
We developed a complete backend workflow to automate video ingestion, processing, audio/subtitle integration, and re-uploading with end-to-end session traceability.
Integrated Firebase SDKs to automate video retrieval and uploads based on structured session metadata while ensuring encrypted access and bucket-level control.
Videos were renamed and routed into pre-defined Firebase folders, ensuring logical storage paths based on test type, user, and timestamp.
Each recording was linked to key metadata like officer ID, result, and test type, supporting downstream analytics and regulatory documentation.
Locally processed videos were enhanced with audio prompts and subtitle overlays by aligning SRT files to specific eye movement cues used in impairment detection.
Processing pipelines supported bulk video handling, download retries, file integrity validation, and optional compression or watermarking for public-facing use.
The system captures real-time eye movement via VR headsets and supports AI-based evaluation of THC-related visual markers during roadside testing.
Session-specific videos are pulled automatically, streamlining the processing workflow and reducing the need for manual intervention.
FFMPEG-powered logic injects synchronized audio and subtitle cues, offering a consistent test structure and improved replayability for legal or training use.
Processed videos are automatically re-uploaded with traceable paths, flag-based visibility (private/public), and expiration logic.
Every session is audit-ready, with encrypted processing, structured logs, and metadata for full visibility into who was tested, when, and how.
FFMPEG
Python
3rd-Party SDK
encrypted data storage
SRT
We modernized subjective roadside testing with objective, VR-based eye tracking sobriety test workflows supported by structured video evidence.
Audio and subtitle overlay automation reduced session processing time by 70%, enabling quicker decision-making at the point of contact.
With a scalable video infrastructure, our client deployed an AI drug testing solution capable of handling large volumes of sessions across jurisdictions.
The system now supports additional developments such as rehabilitation app development and pupil dilation analytics using previously recorded sessions.
With secure handling, metadata tagging, and visibility flags, the platform meets the privacy and audit needs of both public and private sector stakeholders.
Empower your team with next-gen tools that streamline, secure, and scale cannabis impairment detection for real-world impact.
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