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Identify security-related individual targets from range of data including sensors, camera feeds and suspect activity

Identify security-related targets from analysis of sensor feeds (e.g. drone cameras) typically matched with image recognition technology. Increasing debate about the extent to which this is tied to lethal capability and the role of a trained human in the decision loop.

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Risk reduction

Risk reduction - Real time awareness

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Risk reduction

Risk reduction - Predictive diagnosis

Description

Identify security-related individual targets from range of data including sensors, camera feeds and suspect activity

Linked Case Studies

Deep Sentinel identifies neighbourhood security threats using machine vision that can detect potential intruders and other events

US Department of Defense

The US Department of Defense aims at increasing drone effectiveness by analysing collected footage with machine vision

Google''s cocktail party effect uniquely identifies and focuses on an individual''s voice while watching a video of people talking in a crowded room

New York Police Department (NYPD)

The NYPD searched through surveillance footage using facial recognition

US Federal Bureau of Investigation

FBI maintains law enforcement database searchable by facial recognition

Beijing Police identify people by their walking gait from up to 50 metres away

Jiangxi Province Police

Jiangxi Province Police in China used facial recognition technology to identify and arrest a single person of interest in a concert crowd of 60,000

Orlando Police Department

Orlando Police Department cross-references faces against persons of interest in a pilot use of facial recognition technology

National Security Agency

The US National Security Agency achieves false positive rate of 0.008% for identifying potential terrorist accomplices using machine learning

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