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A project log for FaceAssure: cloud based pan-tilt security camera

A security camera product that presents that tracks a person's face using openCV in the cloud.

Nathaniel WongNathaniel Wong 06/11/2019 at 20:040 Comments

Design Requirements

Design Description

Figure: Block diagram of proposed multi-camera system with central cloud server

Using a hub and spoke model will result in a high latency between sensors and actuators, because a TCP round-trip will be required to process each video frame.

The benefit to this is that the central server is capable of higher image processing loads. We will use a combination of Tornado, a lightweight python web server, and OpenCV, a powerful real-time computer vision library built on python, to our advantage on the cloud compute instance. This will allow the viewing and autonomous control of multiple video feeds..

Planning and Organization

Action by

Start Date

End Date

Status

Define project goals

Nat

Apr 2, 2019

Apr 9, 2019

Complete

Research components

QK

Apr 2, 2019

Apr 9, 2019

Complete

Prepare order

Nat

Apr 2, 2019

Apr 9, 2019

Complete

Create new PCB layout

QK

Apr 9, 2019

Apr 11, 2019

Active

Setup Amazon AWS services

Nat

Apr 9, 2019

Apr 16, 2019

Complete

Camera client sends images to AWS

Nat

Apr 16, 2019

Apr 23, 2019

Active

Use breakout board to test LCD&Pan-tilt

QK

Apr 17, 2019

Apr 24, 2019

Upcoming

Put together breakout prototype

QK

Apr 18, 2019

Apr 25, 2019

Upcoming

AWS server processes images with CV

Nat

Apr 23, 2019

May 9, 2019

Upcoming

Make revisions and make embedded PCB

QK

Apr 25, 2019

May 2, 2019

Upcoming

Put together embedded prototype

QK

May 9, 2019

May 16, 2019

Upcoming

Make revisions and design revised PCB

QK

May 16, 2019

May 23, 2019

Upcoming

Pan-tilt base reacts to location of human face

Nat

May 9, 2019

May 30, 2019

Upcoming

Put together final prototype

All

May 30, 2019

Jun 12, 2019

Upcoming

Prepare final presentation

All

Jun 4, 2019

Jun 12, 2019

Upcoming

Figure: Gantt Chart for the project

Item Desc.

Mfg. Part #

Unit Price

1000 Unit Price

Quantity

Mini Pan-Tilt Kit

1967

18.95

15.15

1

LCD screen

181

9.95

7.96

1

Figure: Bill of Materials for the project

Splitting of work

Qiankai will largely focus on the hardware aspects of the design and fabrication process, including PCB design, servo motor control and final integration of different modules.

Nathaniel will design the central server functionality, modify the camera to accept wifi connections to the server and stream images, and implement computer vision techniques.

Results of Market Research

Based on home ownership data, about 40 million middle to upper income urban households own their properties globally, and many of these households face the real threat of petty household theft. To that end, they may find value in an autonomous visual security system that deters thieves while providing homeowners of critical alerts and valuable visual evidence of trespassers.

List of competitors

One major brand is SimpliSafe, which provides an array of household security camera products, entry sensors and motion sensors. At a price of US$500 for a complete solution, this may be prohibitive for even that most security-conscious of users. The products also do not include face detection and recognition

NestCam is another serious competitor that features built-in facial recognition for outdoor cameras, and is able to recognize ‘familiar persons’ with in-app alerts. Cloud storage backs up the video feeds as part of the product package. The camera retails for $349, a hefty price tag for a singular camera, or $600 for a pair.

Other brands: Arlo, Allianca, Blink, Nest Cam, Zmodo, Dlink, Netgear, Logitech

Results of Interviews

We conducted interviews with two potential users, but did not manage to interview an expert. Our interviewees found a product that could recognize specific faces to be very useful in monitoring their private spaces, for example, alerting against front door package thieves or trespassers. On the topic of facial recognition, one interviewee noted that constant face detection alerts could get stale because of false alarms, and suggested using criminal face databases to reduce false alarms

Next Steps

Through signal generator, we have found that duty cycle of square wave stands for position, and 1% duty cycle corresponds to 25 degree. Some parameters are included here, period of 20 ms, amplitude of 3.3 Vpp. Moreover, two axises of motors are controlled by two different channels, we will have to generate two relative signal to control the facing direction of pan-tilt kit. By the next presentation, we hope to provide a working demonstration of the servo motors through PWM signal generated by MCU. Our final goal is  to receive facial recognition information from webcam, and move motor to right position as we plan at the same time.

Another aim is to have a fully working camera module that streams images to the server at a reasonable rate, of at least one every two seconds. Should this not work, one contingency is to use lower resolution images or revamp the TCP protocol used to make it more efficient.

Finally, as a stretch goal we will attempt to demonstrate an initial prototype of the facial classifier that generically classifies faces seen on the camera’s image stream.

References

https://www.cnet.com/pictures/security-cameras-with-facial-recognition-tech-inside/

Discussions