I have made a small collection of training data (currently around 18 images images, in two sets of 9 of people with guns and people without guns) and have made a basic implementation which attempts to guess whether somebody has a gun using support vector machines based off of 128x128 CCTV camera stills of people either carrying a gun or not carrying a gun. The present implementation appears to overfit the data so I will attempt to resolve this; it always correctly classifies everything in the training set but usually errs to the side of false negatives when classifying test data from casual observation.
Progress to be made on this:
Collect more images (ideally 50 with guns, and I can take the rest of the images from pre-existing person reid datasets like ViPER - edit: may need some images from scenes in which guns appear in case the detector becomes influenced by the lighting/other composition of the scene.)
Find out how to regularise(?)/prevent overfitting in the model I've come up with
Formalise the analysis stage.
The source code for this version of the program can be found here:
To be honest, you can get rid of that opencv dependency.