Ashwin Sekar (asekar) and Richard Zhao (richardz)
Most of our time was spent doing background reading on the subject and rederiving the math behind the algorithm we plan to use. This provided us with a solid foundation and outline of our algorithm. We created a reference program that uses OpenCV builtins and created a testing harness to run it.
The next step was to create the baseline C++ implementation that we would eventually port to CUDA.
We took a good deal of time exploring existing implementations such as
OF_DIS and image-align
and decided to iterate on OF_DIS, whose author is the same as [1]. We studied the existing
implementation, cleaned parts of it up, and generally modified it to better suit our needs.
The updated schedule reflects a general shift back of roughly a week as both team members are part of an organization that is heavily involved in Spring Carnival’s Booth. This was accounted for in the original schedule, which had a relatively light workload planned for the final week.
As a result of the schedule shift, we plan to demo our algorithm operating on previously recorded drone footage, with the reach goal being a demo on live drone footage.
OpenCV renders a 1024 x 436 frame in 67ms, whereas our baseline C++ implementation renders the same frame, with smoother output flow, in 15ms.
| Date | Milestone | Done |
|---|---|---|
| April 11 | Complete understanding of the algorithm | ✔️ |
| April 14 | Working OpenCV reference and testing harness | ✔️ |
| April 25 | Working implementation in C++ | ✔️ |
| April 27 | Cleaned up and optimized C++ version | |
| May 1 | Working implementation in CUDA | |
| May 5 | CUDA implementation with same performance as C++ version | |
| May 8 | Achieve performance better than published in [1] | |
| May 9 | Running on example drone footage | |
| May 11 | Final writeup and demo preparation | |
| May 11 | (Reach) Hardware hooked up to drone | |
| May 12 | Final presentation |