In order to get a more accurate depth map, I had to get the matrices for our cameras by calibrating both cameras using a chessboard. Since the relative setup of the cameras will always be constant, the calibration matrices can be saved and used for future parts of the project.
To get distances to objects, there are one of two options, either use sizes of known objects such as car widths or license plates or use a disparity mapping. I decided to start working on the disparity approach which gives a new image of far and near objects, without an exact distance.
Since performance is critical to the application of our project, I thought it’d be important to log our current sampling frame rate by writing on top of the window.
I decided to start off developing the algorithmic part of the car collision project. After a few weeks of debating whether to use C++ or Python, I decided to use Python since there are around 10 weeks left to get this project up and running. I also decided to use OpenCV as it has many of the functions we need to perform and also its highly optimised. Although through my research into OpenCV shows that there is no built in way to get a Depth Map that returns exact distances for each pixel, I decided we can worry about that later after we get the Depth Map working. In a worst case scenario, we can map depth map values to distances or find an alternative. I just wanted to get our feet wet with the project.
After playing around with tinkerOS I decided to give the Raspberry Pi 3b a try and see if I liked it any better. For some reason I did and have decided to use it as the board for the rest of the project.
After deciding to go with the Raspberry Pi route rather than the Mojo FPGA board, I now have decided what peripherals I need in order to make the omputeollision prevention research project a reality. The most important thing was that I now knew I had to work around the Raspberry Pi.
My senior design project as an electrical engineering student is to build a system that prevents car collisions. It started off as a very simple project that alerts drivers if they’re approaching an object quickly but grew into a big project that involves advanced image processing and interviewing to prevent crashes.