Low Latency Camera Feed Development

2023 WINLAB Summer Internship

Project members: Brayden Casaren, Sebastian Clarke, Ayush Iyer, Rohit Kartickeyan

Advisors: Ivan Seskar and Jennifer Shane

Project Goal

To find the method(s) of reducing latency to a minimum in a unicast camera to computer connection over a network.

Project Design

Our camera and LED light is sealed in a box with minimal light. The camera is continuously recording to capture the LED light turning on and off.

The Time Machine is equipped with a GPS and uses PPS (Pulse Per Second) and PTP (Precision Time Protocol). PPS sends a precise voltage that turns the LED light on at the start of every second.

As the camera streams footage to our computer, we can save the incoming video packets through a software called TCPDUMP. These packets contain the data for the captured camera video. When the LED light turns on, the data inside the transmitting video packets will change, indicating that light is now being captured.

The GPS in the device gives a standard source of time. PTP uses this time to timestamp packets with the time the computer receives them, and to sync all the local clocks on our computer network to the GPS standard time.

Weeks 7-8

During these two weeks, we decided to create a histogram to visualize how noise affects packets. Using the Python library Matplotlib, we created…

• Visualizing Data Through Histograms (Matplotlib).
• Uploaded individual frames of the artificial video to the node.
• Made little to no noise.
• Changed LED setup to take up more of the camera’s view.
• Using FFMPEG to get video into one MJPEG or many JPEG files.

Week 7 presentation: ​https://docs.google.com/presentation/d/1oPe0z3FzBUPzzFf-zcCJgz43jQIHqMHRg1BsnYJdaCU/edit

Week 8 presentation: ​https://docs.google.com/presentation/d/1qZLdapSWYrYTItAQqAmdhgF34x_hxIMOjZ_adv8LDIg/edit

Week 9

During this week, we decided to take a step back to understand what the values in the JPEG hex values meant. Once analyzing the hex files from the video, it was quickly discovered there were certain indicators present, being FFD8 and FFD9. FFD8 represented the start of frame while FFD9 represented the end of the frame, while everything inside containing the frame contents. Knowing this, we were then able to isolate specific frames via these indicators via Python.

Week 9 presentation: ​https://docs.google.com/presentation/d/1p3A7ZTJOdkUAqZujGuvOyyJHKzd3qgGuxxUHSaZVm9k/edit#slide=id.p

Week 10

Week 10 presentation: ​https://docs.google.com/presentation/d/1si5gw012hevYePNOeTQiqYPRlw_s4Ao4pM_46wUkApk/edit#slide=id.p

Weekly Progress Reports

Week 1

​https://docs.google.com/presentation/d/1WgLttl-gL1IPvtBatBWwfke9rf4txnYGf04_Hy7mANE/edit#slide=id.p

Week 2

​https://docs.google.com/presentation/d/1rk4QhjhJOKQ2Q0PooV-JtUru1SGpYBeEUab7y3EtSyQ/edit#slide=id.p

Week 3

​https://docs.google.com/presentation/d/1PPJB0SCb0Y8G04pPQRvMS-2uoILxmxJBSVdZgO7B73c/edit#slide=id.p

Week 5

​https://docs.google.com/presentation/d/1eFaGVwyJf6AOELmQVRvLJxMywu_rgNEc2hIgDxk3BDA/edit#slide=id.p

Week 6

​https://docs.google.com/presentation/d/1IwPrkkpvbTHO3LuBZ2Umuxp5rz9jwKTqmml3EoJ7wAA/edit