1

Cognitive Mobile Edge Computing: 

Video Analytics Use Case

Zongru (Doris) Shao

Senior R&D Engineer
2

2

DISCLAIMER

This presentation was produced by guest speaker(s) 

and presented at the National Institute of Standards 

and Technology’s 2019 Public Safety Broadband 

Stakeholder Meeting. The contents of this 

presentation do not necessarily reflect the views or 

policies of the National Institute of Standards and 

Technology or the U.S. Government.

Posted with permission
3

Public Safety Video Applications
4

Mobile Edge Computing 

Cloud Infrastructure

-Large roundtrip delay

-Intermittent/Unavailable

backhaul

Data Center

e.g., LTE, WiFi

Mobile Edge Infrastructure

-Small roundtrip delay

-Backhaul failure resistant

-Local/edge computing

-Edge storage

Data Center

X
5

5G Multi-access Edge Computing for Public Safety Video Analytics
SiFi 200: Integrated Mobile Edge Computing and Networking Router 6

• 2 cellular backhaul links for 

bandwidth aggregation and 

session persistent offloading

• 1 dual-band WiFi for access

• Integrated smart video 

management system (VMS)

• 250 GB for 1-month local 

video storage

• Live video streaming over 

local WiFi

• Cloud video streaming and 

integration with any 

proprietary camera VMS

• AI-based video analytics 

and alerts

• Distributed mobile edge 

computing
2019 Boston Marathon Technology Deployment with Brookline PD 7
8

I haven’t heard of anybody who’s doing a project like 

this...other [agencies] want to wait until something 

has been done and proven, but by that time there’s 

another ‘new thing’ – and you’re already behind.”

— Scott Wilder, Brookline PD

“
Birmingham Operation Shaken Fury Deployment with DHS S&T 9
10

Boston Marathon (April 15, 2019) Deployment Case Study

SiFi 200 deployment location Integrated VMS dashboard
11

Boston Marathon (April 15, 2019) Deployment Case Study

• SiFi 200 device directly connected to a FLIR camera

• Measured and tested Verizon, AT&T, and FirstNet as potential backhaul 

links – chose FirstNet (6 Mbps DL and 3 Mbps UL, ave.) as primary 

backhaul

• FirstNet SIM card provided by Brookline PD

• Live video stream stored locally in the SiFi 200 router and also pushed 

to AWS GovCloud

• Live video streaming over the SiFi 200 WiFi hotspot; outside the 

hotspot via streaming server on GovCloud 

• Video bit rate, frame rate, etc. optimized for FirstNet

• Camera and the SiFi 200 device moved to the final site from a trial site 

by Lan-Tel Communications after finalizing network architecture, 

camera configuration, device configuration, etc.

• Solves poor microwave backhaul 

coverage/reliability problems (6GB of 

uninterrupted marathon video 

streamed)

• Local WiFi edge streaming capability for 

best video quality even without any 

backhaul links

• Multiple backhaul links provide very 

high reliability for mission critical video 

• Easy integration with any proprietary 

VMS

• Support for multiple video streaming 

protocols and video formats

• Plug-and-play technology

Deployment Strategy Value-add for Brookline PD 
12

AI-driven Mobile Edge Video Analytics

Low Cost NVIDIA Jetson Nano GPU for Device Edge Computing
13

AI-driven Mobile Edge Video Analytics

CPU (Intel Core i7-7700) + GPU (NVIDIA GTX 1080) for Aggregation Edge Computing
14

Resilient and Distributed Edge Computing Network Architecture

FirstNet

AT&T/Verizon

Device edge computing

(Quick and lightweight analytics)

Aggregation edge computing

(Complex and deep analytics)

Wired

Wireless

Street camera (Primary

link)

(Secondary

link)

Smart edge gateway
Example Edge Video Analytics 15
Example Edge Metadata Analytics 16

Marathon runners in 

camera’s view
First Responder Video Analytics Use Case 17

Joint work with Andrew Weinert, MIT Lincoln Labs and William Drew, NJOHSP

Generate alert only if: 

(a) night-time and (b) a police officer is lying on the street and (c) crawling

Goal: Reduce cognitive overload on first responders via video analytics on the 

(device or network) edge.

-bodycam video, street side camera feed, drone video, etc. 
First Responder Edge Video Analytics Use Case 18

Video dataset collected and shared by MIT Lincoln Labs/NJOHSP - NIST PSIAP project

Person inside car at night Person outside car at night Person walking 

Person running Person crawling 
19 First Responder Edge Video Analytics Use Case 

• Trained deep machine learning algorithms to model different scenarios 

from video datasets

• Developed machine learning classification algorithms for

situational awareness from video feed

• Generate alerts only if threat detected according to preset policies

• High inference accuracies (>90%) on the available tagged video datasets

• Reduces cognitive overload significantly for first responders

• Transitioned software toolkit to MIT Lincoln Labs
20

Demos

July 10th 10am to 10:45am and 4:20pm to 5:30pm

Resilient Multiaccess Edge Computing for Video Analytics

Public Safety Video Analytics (with MIT Lincoln Labs and NJOHSP)
21

101 Crawfords Corner Road, Suite 101-4R, 

Holmdel, NJ, 07733, USA

Email: info@spectronn.com

Address

Contact Info

Contact

http://www.spectronn.com
Come back for the 

Next 

Session

3:30 PM

22

#PSCR2019