Multi class boosted random ferns for adapting a generic object detector to a specific video

Abstract

Detector adaptation is a challenging problem and several methods have been proposed in recent years. We propose multi class boosted random ferns for detector adaptation. First we collect online samples in an unsupervised manner and collected positive online samples are divided into different categories for different poses of the object. Then we train a multi-class boosted random fern adaptive classifier. Our adaptive classifier training focuses on two aspects: discriminability and efficiency. Boosting provides discriminative random ferns. For efficiency, our boosting procedure focuses on sharing the same feature among different classes and multiple strong classifiers are trained in a single boosting framework. Experiments on challenging public datasets demonstrate effectiveness of our approach.