Cascaded AdaBoost - 2022
Cascaded AdaBoost - 2022
Purpose
Purpose
Construct and testify the cascaded design of AdaBoost classifiers to achieve an arbitrarily low false positive rate.
(Reference: Viola Jones object detection framework.)
Viola–Jones object detection framework
Viola–Jones object detection framework
a boosted feature learning algorithm
trained by running a modified AdaBoost algorithm on Haar feature classifiers.
every weak classifier is a threshold function based on the feature.
the threshold and the polarity are determined in training.
False Positive (FP) and False Negative (FN)
False Positive (FP) and False Negative (FN)
Results
Results
The false positives decrease as expected because the goal of each cascade stage is further to eliminate false positives from the previously identified positives.
The false negatives increase as expected because previously identified negatives are discarded and are not passed down to the next cascade stage.
