Semantic Search

Introduction Video investigation often depends on how efficiently users can locate relevant footage. Traditional VMS search methods rely on structured filters—dropdown menus, checkboxes, and predefined attributes such as color, gender, or object type. While effective, these approaches can be time-consuming and restrictive.

Semantic Search redefines this process by enabling users to describe what they are looking for in natural language. Powered by built-in Large Language Model (LLM) capabilities, the system interprets intent and intelligently retrieves the most relevant video clips.

Concept Overview Semantic Search allows users to input free-form queries such as:

“Find a person wearing a red jacket entering the lobby between 8 and 9 PM.”

The system analyzes:

• User intent and contextual meaning
• Metadata generated from AI video analytics
• Temporal and spatial conditions

It then filters and delivers precise playback results—without requiring manual parameter selection.

Key Capabilities Natural Language Query Users can describe scenarios freely, eliminating the need to navigate complex filter menus.

Intelligent Metadata Mapping The LLM bridges user intent with structured metadata (e.g., object type, color, behavior), enabling more accurate matching.

Context-Aware Search The system understands time, location, and event context, refining results beyond simple attribute matching.

Flexible Refinement Users can iteratively refine searches:

“Only show results at night” “Exclude vehicles” “Focus on the entrance camera”

Benefits Faster Investigation Reduces search time significantly by eliminating manual filter configuration.

Higher Accuracy Captures nuanced queries that traditional filter-based systems may miss.

Improved Usability Makes advanced search accessible to all users, regardless of technical expertise.

Reduced Cognitive Load Users think in scenarios—not system parameters—leading to a more natural workflow.