Adaptation of boosted pedestrian detectors by feature reselection

Adaptation of pre-trained boosted pedestrian detectors to specific scenes is an important yet difficult task in computer vision. To address this problem, a feature reselection strategy is proposed in this paper. The proposed method identifies weak classifiers which do not well adapt to the specific scene, and replaces them with retrained weak classifiers. This feature reselection strategy has the following advantages: 1) it does not need original offline training data, but only uses a few online samples from the target scene; 2) the adapted detector preserves the generality of the generic detector, resulting in very few false positives; and 3) it can adapt a generic detector to a specific scene with very fast speed due to its parallel nature. Experiments on challenging pedestrian detection datasets demonstrate that our proposed strategy can significantly improve the performance of pre-trained boosted detectors in specific scenes with very low computation cost and very little labeling work.