Training a real usage cascade is not trivial, number of samples and model size are fundamental in creating something meaningful. For example, for an eyes cascade consider:
- 9,000 positive samples
- 0.7 B of neg. sub-regions with negatives samples.
- Features set: 85.550 features allocated.
- Training time: ~1 days
- TP: ~ 95.8% of positive training set
- FN: ~ 04.2% of positive training set
- FP: ~ 7.51937e-006% of negative training set
- Training size w=30 h=60 (aspect ratio 1:2)
Full reports:
- eyes: http://www.vision-ary.net/2015/11/boost-the-world-eye-detection-lbp-hog-haar-cascade-opencv
- faces: http://www.vision-ary.net/2015/03/boost-the-world-face-detection/
- cars: http://www.vision-ary.net/2015/06/boost-the-world-car-detection/
- pedestrians: http://www.vision-ary.net/2015/03/boost-the-world-pedestrian/
