Join our meetup, learn, connect, share, and get to know your Toronto AI community.
Browse through the latest deep learning, ai, machine learning postings from Indeed for the GTA.
Are you looking to sponsor space, be a speaker, or volunteer, feel free to give us a shout.
Half a year ago, when I lived in an apartment with a view of Ayalon Road in Tel Aviv, I decided I couldn’t just let the data pass indifferently beneath my window.
I used my smartphone camera to record 81 short videos of the traffic; built a dedicated CNN to detect the vehicles (after the small, crowded cars in the videos were failed to be detected by several out-of-the-box networks) and trained it on a few manually-tagged video-frames; modified SORT algorithm to allow tracking a vehicle even when it does not overlap itself over adjacent frames (required due to particularly low videos frame-rate); and derived several insights from the resulted data, mainly regarding lane-transitions and the relations between density, speed and flux.
I believe that this nicely demonstrates the amounts of data surrounding us, and their accessibility using as trivial tools as a smartphone.
Any comments, questions and insights are welcome 🙂
A small demonstration is attached in the form of an unpolished poster.
For more details please visit the repo’s readme: