HAAR LBP HOG Pedestrian Detection with OpenCV
HAAR LBP HOG pedestrian detection is not a trivial task, especially if you want to perform it on ARM devices. Before using the following cascades read carefully this page to get the best performance and to know the terms of usage.
The following freely available cascades largely outperform the OpenCV built-in HOG detector and the HAAR cascades included in OpenCV. Take a look to the next clip to see a demonstration (91% our cascade precision vs. 51% OpenCV).
In black: manually marked persons.
In green: detections that are considered correct.
In red: detections considered incorrect (false positives).
Full frontal/rear (with partial profiles) pedestrian detector, trained with:
- approx. 47,000 positive samples (randomly sampled)
- approx 1.1B of negative sub-regions containing outdoor and indoor samples (80%-20%)
- Training size w=26 h=74 (aspect ratio ~1:3)
- Features set: 97.200 features
- Training time: ~4 days
- TP: ~ 94.51% of positive training set
- FN: ~ 05.49% of positive training set
- FP: ~ 6.8e-006% of negative training set
HOG: (contact us)
- Features set (approx.) : 100 features
- Training time: ~3 days
- TP: ~ 93.87% of positive training set
- FN: ~ 06.13% of positive training set
- FP: ~ 1e-006% of negative training set
For more info about OpenCV cascades take a look to the introduction on hyper-fast HAAR, LBP, HOG cascades in OpenCV