AR Incident Data Overlay for Suspect Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In public-safety incidents, officers face challenges in quickly identifying individuals with relevant criminal histories among numerous witnesses or participants, leading to information overload and potential misses in critical suspect identification.
Innovation Solution
An augmented-reality system that uses facial recognition and a database mapping incident types to relevant criminal histories, highlighting individuals with relevant pasts through virtual objects, shading, text, or colors on an officer's device, ensuring only pertinent information is displayed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all individual data is displayed at the incident scene, then complete information is provided to the officer, but information overload occurs making it difficult to identify critical suspects
Solution Approach 1:
The patent applies local quality by displaying different visual characteristics for different individuals based on their relevance to the incident. Individuals with matching criminal histories are highlighted with specific visual markers (e.g., red outlines, icons), while others are displayed with different or subdued characteristics. This allows the officer to quickly identify critical suspects without being overwhelmed by uniform presentation of all data.
Solution Approach 2:
The system extracts and separates critical information from the complete dataset by identifying individuals whose criminal histories match the incident type and displaying them with prominent visual markers. This extraction principle allows the officer to focus on relevant suspects while the complete information remains available but visually differentiated in the background.
2Measurement precision
If facial recognition and criminal history database are used for all individuals, then accurate suspect identification is achieved, but system complexity and processing time increase
Solution Approach 1:
The system performs preliminary action by pre-establishing mappings between incident types and relevant criminal history categories before the incident occurs. When an incident happens, the system only needs to check individuals against pre-defined relevant criteria rather than analyzing complete criminal histories, thereby maintaining high accuracy while reducing processing complexity and time.
Solution Approach 2:
The patent segments the criminal history database into categories relevant to specific incident types. Instead of processing complete criminal histories for all individuals, the system divides and checks only the relevant segments (e.g., violent crimes for assault incidents, property crimes for burglary incidents), reducing system complexity while maintaining identification accuracy.
3Loss of information
If detailed criminal histories are displayed for all individuals, then complete background information is available, but the officer cannot quickly identify relevant suspects
Solution Approach 1:
The patent uses color changes and visual markers to indicate relevance. Individuals with matching criminal histories are displayed with distinct visual characteristics (e.g., red outlines, warning icons, highlighted text) that allow instant recognition. Complete criminal history data remains available for review, but the visual encoding enables rapid identification of relevant suspects, reducing response time without losing information completeness.
Data Source
AI summary
A method and apparatus for displaying data at an incident scene is provided herein. During operation an officer will be assigned to a particular incident having an identified “incident type”. Individuals at the incident scene are identified via facial recognition and criminal histories for all recognized individuals are obtained. The incident type will be used to determine criminal histories relevant to the incident type. Only the criminal histories of those individuals at the incident scene that are relevant to the incident type will be displayed (ideally displayed proximate to the identified individuals).