AI-Powered Video Analytics for Law Enforcement
Description
The AI is intended to solve the challenge of manually reviewing large volumes of surveillance video, which is time-consuming, labor-intensive, and prone to human error.
Detailed example
- Object-level detections: bounding boxes with classifications (e.g., person, vehicle type, animal), attributes (e.g., clothing color, bag, face mask), and movement patterns. - Appearance-based search results: lists of matching individuals or vehicles based on facial features, clothing, or license plate. - Real-time alerts: triggered events based on predefined rules (e.g., line crossing, group formation, presence of a vehicle type), sent via connected systems.- - Visual summaries: Video Synopsis® clips that compress hours of activity into short, layered visualizations for faster review.- - K34Dashboards and analytics: aggregated data on movement, dwell time, crowding, object counts, and traffic patterns to inform operational decisions.
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Faster identification of persons, vehicles, and events of interest through AI-powered video search and filtering. Improved accuracy and objectivity in surveillance review and analysis. Increased situational awareness via real-time alerting and behavior detection. Greater operational efficiency, enabling limited staff to manage larger video workloads. Data-driven decision-making supported by trend analysis and visual dashboards.
Audit / financial statement impact
AI is used to filter relevant video, but outputs are verified by humans, decisions/actions are performed by humans
Controls / human review
ATO: Yes; PIA: Not published