Title

SecureVis

Lead Author Major

Computer Science

Lead Author Status

Senior

Second Author Major

Computer Science

Second Author Status

Senior

Third Author Major

Computer Science

Third Author Status

Senior

Format

SOECS Senior Project Demonstration

Faculty Mentor Name

Shon Vick

Faculty Mentor Email

svick@pacific.edu

Faculty Mentor Department

Computer Science

Additional Faculty Mentor Name

Osvaldo Jimenez

Additional Faculty Mentor Email

ojimenez@pacific.edu

Additional Faculty Mentor Department

Computer Science

Abstract/Artist Statement

SecureVis is an open source smart computer vision driven security system that leverages an edge computing paradigm and deep learning computer vision algorithms to determine human activity across multiple spaces. Multi camera systems can be configured across various rooms / spaces, allowing for maximum coverage across a users property. Small camera clusters can be configured to communicate with a microcomputer (e.g. raspberry pi) which uses low computational cost OpenCV algorithms to determine if there is considerable movement within each cameras optical view. If such movement is detected data is passed over the network to a master system which utilizes a GPU accelerated Regional Convolutional Neural Network (e.g. YOLO) to determine if the subsequent activity is human. This human activity then triggers the affected cameras to stream security footage from their respective edge systems to the master system. This data is saved locally on the master system. Due to the nature of the edge-based design this allows a user to configure multiple camera clusters across their property creating a smart connected security system that is easily expandable for a relatively low price, this cluster-based system will also allow users to toggle specific areas of their property depending on their circumstances. The end user will have graphical interface that will make it simple for them to review and manage their recorded security footage.

Location

School of Engineering & Computer Science

Start Date

4-5-2018 2:30 PM

End Date

4-5-2018 4:00 PM

This document is currently not available here.

Share

COinS
 
May 4th, 2:30 PM May 4th, 4:00 PM

SecureVis

School of Engineering & Computer Science

SecureVis is an open source smart computer vision driven security system that leverages an edge computing paradigm and deep learning computer vision algorithms to determine human activity across multiple spaces. Multi camera systems can be configured across various rooms / spaces, allowing for maximum coverage across a users property. Small camera clusters can be configured to communicate with a microcomputer (e.g. raspberry pi) which uses low computational cost OpenCV algorithms to determine if there is considerable movement within each cameras optical view. If such movement is detected data is passed over the network to a master system which utilizes a GPU accelerated Regional Convolutional Neural Network (e.g. YOLO) to determine if the subsequent activity is human. This human activity then triggers the affected cameras to stream security footage from their respective edge systems to the master system. This data is saved locally on the master system. Due to the nature of the edge-based design this allows a user to configure multiple camera clusters across their property creating a smart connected security system that is easily expandable for a relatively low price, this cluster-based system will also allow users to toggle specific areas of their property depending on their circumstances. The end user will have graphical interface that will make it simple for them to review and manage their recorded security footage.