Ashwin Sekar (asekar) and Richard Zhao (richardz)
We implement real time optical flows on a mobile GPU platform using the dense inverse search method.
A common problem in computer vision is detecting moving objects on a background. With an increasing amount of cameras mounted on moving vehicles, stabilization of the video feed is a crucial preprocessing task.
Optical flows present an elegant solution to a wide class of problems such as the above. An optical flow is a vector field that describes per-pixel displacements between two consecutive video frames in a video feed.
In recent years, there has been increased interest in algorithms for computing optical flows, especially ones that achieve a mix of efficiency and accuracy. Kroeger et. al. propose a method with very low time complexity and competitive accuracy for computing dense optical flow[1].
The algorithm is highly parallelizable, which gives it the potential to achieve super-real-time (faster than 30 Hz) performance on GPUs.
make flow_ref[1] Tim Kroeger, et. al Fast Optical Flow using Dense Inverse Search (2016)
| Date | Milestone | Done |
|---|---|---|
| April 11 | Complete understanding of the algorithm | ✔️ |
| April 14 | Working OpenCV reference and testing harness | ✔️ |
| April 25 | [Checkpoint] Working implementation in C++ | ✔️ |
| April 27 | Cleaned up and optimized C++ version | ✔️ |
| May 1 | Working implementation in CUDA | ✔️ |
| May 2 | CUDA implementation with same performance as C++ version | ✔️ |
| May 4 | Realtime performance (~30fps / < 33ms) | ✔️ |
| May 8 | Super-realtime performance (~30fps / < 10ms) | |
| 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 |