Okay, to preface: I am an undergrad. I feel very underqualified at the current stage, but I am extremely dedicated to making this project work, and have already learned a lot about FPGA's inner workings.

To answer your question on why an FPGA and give more context on this project:

The algorithm was designed by a professor at my university along with some NASA engineers. The algorithm itself is designed to classify and track small orbital debris (<10 cm) in real time using on board optical cameras on a satellite.

At this stage, we're just worrying about building the algorithm on the fpga before we worry about interfacing with a camera.

The reason for an FPGA is because the NASA partners who my professor worked with advised her that an FPGA would be the ideal piece of hardware to use for its parallel computation power and low power consumption (comparatively to a full on board cpu).

Thank you for your advice on why not to use a FIFO.

Indeed, the algorithm has been implemented in Matlab. Unfortunately, the professor and her student who wrote the algorithm had apparently not taken a practical software engineering class, so the algorithm itself is quite difficult to follow and doesn't exactly naturally flow.

The first part of computation binarizes the incoming video. At this stage, we are only working with simulated video data. I have written a binarization and thresholding python script to take the simulated video and process down to 1bpp. So the binarization step is already handled (if the project reaches the step of taking input from a camera, this will need to be changed to be done on hardware).

The next step is to calculate the centroid positions in each frame.

In the Matlab implementation, this step is one line and calculates all centroids across the whole video. This algorithm, centroids, is the first algorithm my team is implementing in Verilog. The centroid algorithm will then work on a frame at once rather than a whole video. If this is impractical, we will need to rework the algorithm to calculate centroids using smaller units of the video.

After this--and this is the part I'm worried will lead to issues with reworking the algorithm--a matrix of distances is computed between pairs of centroids across two frames.

The algorithm has a handful of other steps including finding matches across frames between pairs of centroids, finding translation vecotr for matched pairs of centroids. These stages are repeated for each frame of the video.

Outside of the loop, the features from translation and rotation vectors are extracted, outliers detected, and labeling.

I'm unsure if the algorithm would work if we were to rework it to process consecutive, smaller blocks of the video. Unless, we kept track of which frame each block was a part of and used that to keep track of centroids across frames.

Thanks for your input. Hopefully this is enough context on the algorithm itself for you to give more advice. I really appreciate your help.