Date of Award

3-2022

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Electrical and Computer Engineering

First Advisor

Barry E. Mullins, PhD

Abstract

The prevalence of Light Detection and Ranging (LiDAR) sensors in consumer cellphones has ushered in an era of access to technologies previously inaccessible to the average person. Although LiDAR sensors emit light in the infrared spectrum invisible to the naked eye, many security cameras possess the ability to capture infrared light reflected off surfaces to detect LiDAR light in an environment. Once detected and recorded to video footage, current classification methods fail to identify the LiDAR light. Therefore, this research develops a methodology to detect LiDAR light in security camera video footage and identify it as cellphone LiDAR. A proposed image processing technique separates the LiDAR light pattern from the background of a video frame. The extracted LiDAR light pattern is then permuted many ways to simulate how it may appear in video footage. A neural network trains to identify these permutations which results in a neural network that classifies whether or not a given image contains the LiDAR light pattern. In a dark environment with a cellphone and security camera positioned 1 meter from a wall, the trained neural network correctly identifies 99.7% of images containing cellphone LiDAR reflected off the wall.

AFIT Designator

AFIT-ENG-MS-22-M-019

DTIC Accession Number

AD1166851

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