Machine Learning for OpenCV 4 - Second Edition

Bootstrapping the model

An interesting way to improve the performance of our model is to use bootstrapping. This idea was actually applied in one of the first papers on using SVMs in combination with HOG features for pedestrian detection. So let's pay a little tribute to the pioneers and try to understand what they did.

Their idea was quite simple. After training the SVM on the training set, they scored the model and found that the model produced some false positives. Remember that false positive means that the model predicted a positive (+) for a sample that was really a negative (-). In our context, this would mean the SVM falsely believed an image to contain a pedestrian. If this happens for a particular image in the dataset, this example ...