Background Subtraction

We have tried three models for this problem:

1. Mixture of Gaussians
2. K Nearest Neighbours

Running Mode

Generate the prediction masks

python main.py  --inp_path=<path to input frames> --out_path=<path to generated masks> --eval_frames=<path to eval_frames.txt file> --category="<b/i/j/m/p>"

Evaluate the prediction masks against ground truth

python eval.py  --pred_path=<path to generated masks folder> --gt_path=<path to groundtruth masks folder>