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Low Latency Camera Feed Development
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.
Week 5
Summary
-Created Python script
Week 5 presentation: https://docs.google.com/presentation/d/1eFaGVwyJf6AOELmQVRvLJxMywu_rgNEc2hIgDxk3BDA/edit#slide=id.p
Week 6
Summary
-Created artificial videos
-Planned to create a histogram using Matplotlib.
Main Idea
This week was shortened to a considerable margin to the point where we couldn't make much progress. However, we were able to plan for the following weeks by creating many artificial videos of varying amounts of noise as well as a plan to create histogram to plot the number of occurrences each hex byte appeared in order to visualize how noise affects packets using the Python library Matplotlib.
Week 6 presentation: https://docs.google.com/presentation/d/1IwPrkkpvbTHO3LuBZ2Umuxp5rz9jwKTqmml3EoJ7wAA/edit
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
Summary
- Learned about JPEG hex data indicators
- Wrote Python scripts to isolate frames
- Calculated Latency
Main Idea
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.
After this step, we were finally able to calculate latency. By comparing the frame data to the packet data, we could find what exact packet correlates to the frame. By using the timestamp of the packet, we can then be able to find the latency.
However, this didn't come without challenges. For one, both the frame data and the packet data have many values that all need to equal to each other. Moreover, much of the packet data could be similar to the frame data with a few differences, making it extremely difficult to determine whether they match or not. As a result of these difficulties, we ended up developing a Python script that can determine which packet matches with the frame. With these challenges overcome, we were then able to find the latency of the camera of being about 45 milliseconds.
Week 9 presentation: https://docs.google.com/presentation/d/1p3A7ZTJOdkUAqZujGuvOyyJHKzd3qgGuxxUHSaZVm9k/edit#slide=id.p
Week 10
Summary
-Finalized poster
-Finalized final presentation
-Reduced latency
Main Idea
When it came down to our final week of the project, it mainly revolved around finalizing the presentation and poster. However, there were other things that needed to be done, the main one being a way to reduce latency. When it came to reducing latency, this we could do via changing the camera settings in areas such as codec, data compression rate, or contrast.
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
