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Neat.. A while ago I was trying to figure out how to run tensorflow on videos from the camera I have in front of my house. I had worked out how to pre-process
by justinsaccount 7y ago
Neat.. A while ago I was trying to figure out how to run tensorflow on videos from the camera I have in front of my house.
I had worked out how to pre-process the video using opencv to mask out whatever was moving and export that to static images. Then train TF using those images.
The resulting system worked enough to say that a USPS truck was outside, but couldn't tell the difference between the truck driving by vs stopping to deliver something in the mailbox.
I had an idea to use opencv to track the objects, then export a graph of the X,Y coordinates of the moving object over time, and then train TF on the graphs. but never got around to testing it.
I wonder if this project would do a better job, or still have issues because all the videos are almost identical.
- MasterScrat 7y agoOne way to solve this problem is to give your neural network a stack of successive frames, instead of given them one by one. If the framerate is too high, you may need to skip some of the frames to get idea of speed/acceleration. See eg the concepts of "frame stack" and "frame skip" in reinforcement learning: https://danieltakeshi.github.io/2016/11/25/frame-skipping-and-preprocessing-for-deep-q-networks-on-atari-2600-games/ https://danieltakeshi.github.io/2016/11/25/frame-skipping-an...
- learc83 7y agoCouldn't you check to see if you detected the truck for more than x frames?