Early this year I started a project, TrafficFlow. The goal was to train a neural network to recognize, with reasonable accuracy, congested traffic conditions. I needed data, so every 3 minutes I was sampling a NC DOT traffic camera at I-40, exit 289.
This camera, and many others, can rotate.
(Above are images taken from the same traffic camera, one when it’s facing West, the other Facing East)
It would be interesting to see how the neural network responds to this type of data. My guess was I would have ended up with a low accuracy because of the differing angles and most times, there was not a lot of data samples depicting traffic congestion. I would have needed a lot more training data. This forced me to find a new camera and one that is fixed. I found one in Downtown Durham.
It’s even reasonably lit at night. I’ll be sampling this traffic camera every 3 mins, meaning I’ll have enough data (hopefully) to start training a neural network in a week or two. In the meantime, I’ll be doing a few exercises in deep learning, starting with this, Building powerful image classification models using very little data.