Start by clarifying requirements and constraints, then design a scalable, multi-modal pipeline that ingests diverse video sources, extracts rich metadata and embeddings, and enables semantic search and reasoning over footage. Focus on the unique challenges of law enforcement: data sensitivity, chain of custody, and the need to surface evidence-relevant moments rather than just summaries.
Pro tip: Emphasize the importance of a human-in-the-loop approach and explainability, as law enforcement decisions require transparency and accountability. Also, discuss how you would handle false positives and ensure the system augments, rather than replaces, investigator judgment.
Ask questions to understand scale (e.g., petabytes of video, number of concurrent users), latency needs, data retention policies, and legal/compliance requirements (e.g., chain of custody, privacy). Identify key use cases such as searching for a person, object, or event across multiple video sources.
Outline a scalable ingestion layer that handles diverse formats and sources, with preprocessing steps like transcoding, frame extraction, and metadata tagging. Incorporate computer vision models for object detection, action recognition, and face recognition (with privacy safeguards), and generate embeddings for semantic search.
Propose a storage solution that balances cost and access speed, such as object storage for raw video and a vector database for embeddings. Design an indexing strategy that supports fast similarity search and filtering by metadata (time, location, camera type).
Describe how investigators can query the system using natural language or example clips, and how the system retrieves and ranks relevant moments. Include a reasoning layer that can correlate events across multiple videos and surface evidence with explanations.
Discuss access controls, encryption, audit logs, and compliance with regulations like CJIS. Explain how the system scales horizontally and handles failures, and how it ensures low-latency search over massive archives.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.