Efficient incremental learning of boosted classifiers for object detection

Significant progress has been made towards learning a generalized offline object detector. However, when a generalized offline detector is applied on new datasets, it often misses some instances of the object or produces false alarms in the background scene. we propose a novel and efficient incremental learning method, which improves the performance of an offline trained detector. Our approach adjusts the parameters of offline trained cascade of boosted classifiers using manually labeled online samples. Experiments demonstrate both efficiency and effectiveness of our approach.