License Plate Capture and Analysis

Overview

Development stage

DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.

High-impact designation

High-impactSource: a) High-impact

Impact justification

Not reported

Start date

2025-09-19 (day precision)Source: 2025-09-19T00:00:00

Withheld from public reporting

Not reported

Mission

Topic area

Law Enforcement

Operational functions

Document processing and extraction, Summarization, Translation and transcription Derived by keyword rules; see methodology

Problem the AI is intended to solve

The AI is intended to solve the problem of time-consuming manual reviews of license plate images and data, which makes it challenging for investigators to identify relevant vehicle movements and patterns.

Expected benefits

The AI capabilities reduce the need for manual review of large numbers of license plate images and logs. By streamlining plate reading and providing flexible search and summarization tools, the system helps investigators more quickly identify potentially relevant vehicle movements and patterns that might otherwise be missed, thereby improving the efficiency and effectiveness of investigative work.

System outputs

The system processes images and metadata from ICE-owned and commercial license plate recognition cameras. It uses computer vision and optical character recognition to detect and read license plates and to capture associated information such as time, location, vehicle make and model, color, and visible characteristics like damage or signage. An integrated natural language interface powered by a large language model allows users to ask questions in everyday language, such as requesting detections of a particular plate or vehicle description over a period of time. The system converts these questions into structured database queries and returns relevant records, along with concise text summaries of vehicle movements. The system’s AI-enabled outputs are machine-read license plate numbers with associated time, location, and vehicle metadata, as well as natural language search results and summaries produced by the LLM interface. The LLM translates user questions into structured searches over the LPR data and summarizes relevant vehicle detections into concise descriptions of vehicles and their sightings. While license plate information can be used as a link to other personally identifiable information, the LPR system does not automatically link license plate records to driver or vehicle registration databases. Any such queries must be conducted separately in accordance with applicable laws and policies.

Technology

AI classification

Computer visionSource: Computer Vision: AI that processes and interprets visual data (e.g., images and videos).

System name(s)

Not reported

Custom-developed code

No

Public source code

Not reported

Sourcing

How it was built

Purchased from vendorSource: a) Purchased from a vendor

Vendor (as reported)

Motorola

Vendors (standardized)

MSMotorola Solutions

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Yes

Privacy Impact Assessment

www.dhs.gov ↗

Authorization to Operate

No

Demographic features

Not reported

Training and evaluation data

The vendor trained its LPR system using a combination of real-world traffic camera footage, synthetic plate images, and public datasets containing diverse license plate formats from various regions. The models are optimized for high accuracy in different lighting, weather, and motion conditions, and are fine-tuned using data from deployments across cities and agencies.

Federal Data Catalog

Not reported

Governance

8 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

In-Progress - potential impacts will be identified during AI Impact Assessment.