Assignment

Any model Raspberry Pi, including the Pi Zero W, can be used for this miniproject. The essential task is to estimate relative numbers of automobiles traveling a segment of road vs. time. In general, we don't have enough Pi CPU computing power to track cars frame-by-frame or use advanced machine learning techniques. We can instead sample periodically and assume cars we already counted have passed out of our region of interest.

Grade segment breakdown:

• 10%: automobile traffic rate program runs by itself, logging reduced data to disk or cloud upon connecting DC power to the Pi without human intervention
• 5%: Pi has SSH server running for remote management
• 10%: acquiring video on the Pi using the PiCamera
• 15%: logged data rate is less than about 10 kilobytes per minute. It's fine to also store video for debugging.
• 20%: the video processing to relative automobile traffic rate is done on the Pi. The staff may run your code on a Raspberry Pi as part of the evaluation to reproduce your results.
• 15%: Plot for each processed video segment the estimated number of cars traveling the road segment per time interval. The plot can be generated in cloud or on laptop, using data uploaded to the cloud or retrieved from the Pi (e.g. over SSH).

Report

25% of the miniproject grade is for the report. Roughly the equivalent of a 1-2 page report is expected. We aren't concerned with exact report length, just get your message across well.

Please make the report as a Wiki within your miniproject GitHub repo. Your GitHub code & report is due Friday Sept 20, 2019.

In the report, explain your results with content including:

• Estimate the system performance with regard to false detection and false negatives, i.e. compare with what you counted by manually looking at a short segment of video.
• How do you think the system could be improved? (E.g. accuracy, energy)
• explain a bit about your chosen algorithm. Chances are you're using library functions--why did you choose them? (Performance, accuracy, ...)
• plot the relative traffic rate vs. time

When grading, we are assuming this is perhaps the first time you've tried to process streaming video.

Individual contribution

The final individual grade is based on each student's contribution. Your team should indicate in your Wiki and by the Git commit history who did what. For example:

• Jane: coding for video acquisition, autosaving processed data to disk
• Jack: installed OS, optimized counting algorithm parameters