Overview#
A detective investigating a vehicle theft describes the suspect's car and approximate clothing to the system in plain language. Within seconds, the platform surfaces the relevant clips from across a city-wide camera network, with each result timestamped to the precise moment the described subject appears in frame. No camera operator needs to manually scrub through hours of footage, and no rigid metadata tag needs to have been applied at ingestion time.
Semantic Surveillance Search enables operators to query video archives and live feeds using natural language rather than structured filters or manual review. The capability maps visual content and textual descriptions into a shared semantic space using cross-modal embedding models, allowing analysts to retrieve footage by describing what they are looking for in the same terms they would use to brief a colleague. The system processes and indexes footage continuously, supporting sub-second retrieval across archives of any scale.
Key Features#
- Natural Language Queries: Operators describe subjects, vehicles, behaviours, or events in plain language and receive relevant footage without needing to know camera identifiers, timestamps, or tag structures.
- Cross-Modal Semantic Embeddings: Visual content and textual descriptions are projected into a shared vector space, enabling accurate retrieval even when footage was never manually tagged.
- Temporal Localisation: Search results pinpoint the exact start and end timestamps of the matched event within each clip, so analysts go directly to the moment of interest.
- Anomaly Discovery: Abstract behavioural queries such as "erratic movement near restricted access" or "unattended object in concourse" surface deviations from established norms without requiring predefined alert rules.
- Scalable Continuous Indexing: Live feeds and historical archives across large camera networks are indexed in near real-time, maintaining sub-second query latency as the archive grows.
- Hybrid Metadata Search: Semantic intent can be combined with hard structured attributes such as licence plates, camera zone, or time window, narrowing results without sacrificing recall.
- Alert-Driven Contextual Retrieval: Incoming alerts automatically trigger retrospective searches that attach relevant pre-incident and post-incident footage to the triage record.
Use Cases#
- Criminal investigations: Detectives locate footage of a fleeing suspect across a city network by describing the suspect's vehicle and attire, cutting hours of manual review to a targeted set of clips.
- Perimeter and facility security: Guards verify reported security breaches by querying historical footage for the reported characteristics and confirming or ruling out the incident quickly.
- Counter-terrorism and public safety: Analysts search for specific behavioural patterns or object configurations across multiple venues without disclosing operational search terms to camera operators.
- Post-incident forensic reconstruction: Investigators reconstruct a timeline of events by issuing a sequence of natural language queries that progressively build a spatial and temporal picture of movements.
- Anomaly-based patrolling: Security teams surface unusual behaviour patterns during off-hours or in high-risk zones without manually defining every possible alert condition in advance.
Integration#
Semantic Surveillance Search operates as part of the broader intelligence platform and connects directly with the video metadata extraction module to support hybrid queries that blend semantic intent with structured attributes such as licence plate recognition results, camera identifiers, and geographic zone tags. Alert management integration means that any incoming triage record can be automatically enriched with the most relevant footage context before an analyst opens it. The capability exposes a standard query interface so that downstream investigation workflows, reporting tools, and collaboration workspaces can surface footage results inline without switching context.
Open Standards#
- ONVIF Profile S and Profile T: The capability ingests video streams from IP cameras using the ONVIF open protocol, ensuring compatibility with hardware from any conformant vendor without proprietary drivers.
- ISO/IEC 15938-5 (MPEG-7 Visual): Descriptors for visual content characterisation inform how low-level features are represented before embedding, aligning the platform with established multimedia content description standards.
- W3C SPARQL / RDF: Where footage metadata is linked to entity graphs, SPARQL-compatible query patterns are used to express relationships between subjects, locations, and events across the knowledge graph.
- OpenSearch (Apache 2.0): The underlying vector and keyword index layer builds on OpenSearch, an open-source, community-governed search engine, avoiding vendor lock-in for the retrieval tier.
- NIST SP 800-188 (De-Identification of Personal Information in Video): Guidance from this standard informs the platform's approach to handling personally identifiable visual information in compliance with data protection obligations.
- IEEE 802.1X (Port-Based Network Access Control): Camera network ingestion endpoints enforce 802.1X-compatible authentication to prevent unauthorised devices from injecting data into the indexing pipeline.
- ETSI EN 303 645 (Cyber Security for Consumer IoT): Camera device onboarding and stream authentication follow security baseline requirements aligned with this ETSI standard.
Availability#
- Enterprise Plan: Included
- Professional Plan: Available as an optional add-on; query volume and camera feed limits apply
Last Reviewed: 2026-05-26