Category Archives: Car Collision Prevention

Car Collision Prevention Project – Switching to an Ultrasonic Sensor from Stereo Vision

After calibrating and validating the results of a rectified stereo pair, I found that the Raspberry Pi’s performance isn’t capable of running the script effectively. Running the rectification script on a very small image along with the StereoBM function was taking around 3 seconds to process each frame. This obviously wasn’t ideal since we couldn’t use a computer to do the calculation as it had to portable, so I decided to switch over to using an Arduino with an ultrasonic sensor.

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Car Collision Prevention Project – Calibrating the Cameras to Be Used with SGBM

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.

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Car Collision Prevention Project – Disparity using OpenCV

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.

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Car Collision Prevention Project – Getting a Case to Hold the Cameras

The relative position of the cameras for the car collision project is absolutely essential in order to make sure we aren’t changing the parameters of our setup. In order to keep this factor constant before setup the depth map in our software, I wanted to make sure I had something to hold our cameras together. I was going to create my own holder but saw there was a plethora of available models out online specifically for the c270 camera. I decided to print them all out and give a run down about each one.
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Car Collision Prevention Project – Starting off Displaying Two Webcams Using OpenCV in Python

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.

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Car Collision Prevention Project – Setting up a Raspberry Pi 3b and Deciding to Use It as the Main Board for the Project + Installing OpenCV

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.

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Additional Parts for Mechanical Part of the Crash Prevention Project

The final set of parts for the crash prevention project have arrived. These parts deal more with the mechanical side of things and we’ll use them near the last phase of our project where we have to trigger a brake leaver to stop the car.

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Final Selection of Parts for the Computer Vision Collision Prevention Project

After putting a lot of thought into what parts we need for the Crash Prevention Project, we decided on the following parts. Although we could have gotten better parts, we were restricted by budget and time which is the main factor that went into deciding which parts to get.

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