For the past two days, me and my partner have been working on the Raspberry Pi and studying how it works. After getting used to the Linux based OS I started working on Raspberry's IDE which is called Python IDLE. I then installed the OpenCV library which is a process that took 4.5 hours! After adjusting some settings, we tested the face detection with the USB webcam using OpenCV algorithms. The results were as read and expected as the Raspberry did not have enough processing power to execute the face detection in real time. The frame-rate was slow with refresh rates at every half second. The video shows the experiment while the pictures below give a more clearer view of the program in action.
It works!
But the refresh/frame rate was too slow
This effectively rules out the webcam as the input device. However, after further researching, I have found these links that makes use of a Raspberry module that can make the object/lane marking detection possible.
As seen in the video, the feed is so much smoother and would suit our application as split second reactions are so important. So I went and purchased this module for $50 which adds the total to $190. This is still under the budget. This is PiCamera shown below.
PiCamera
The next phase is to follow the links above to develop the object tracking programme demonstrated a month ago.
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