It will also run on computers without GPU i.e. it will run on your processor giving you very poor FPS(around 0.6 to 1FPS), you can use AWS too. One needs to collect lots of images of the artifacts or objects for training the model.
Once the training is done you can simply use the model by editing the 'options' in webcam files and labels of your object.
- It continuously tracks the artifact.
- Alarm triggers when artifact goes missing from the feed.
- It marks the location where it was last seen.
- Captures the face from the feed of suspects.
- Alarm triggering when artifact is disturbed from original position.
- Multiple feed tracking(If artifact goes missing from feed 1 due to occlusion a false alarm won't be triggered since it looks for the artifact in the other feeds)
git clone
python35 .\setup.py build_ext --inplace
- Create a
binfolder. - Download yolov2 weight and tiny-yolo-v2 weight files from here and save inside
bin.
python35 .\webcam-without-alarm-more-fps.py
Watch the demonstration (click me)
If you liked this Repository, then please leave a star on this repository. It motivates me to contribute more in such Open Source projects in the future.
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