关键词: 在线学习, 集成学习, 目标检测, Gentle AdaBoost算法

Abstract: A new online ensemble learning method is proposed for object detection in video. In this method, object detection is considered as two-class classification problem. Firstly, an off-line primed ensemble classifier should be trained with a few labeled samples, and then the false alarm targets will be filtered by tracking while detecting the objects, at the same time, the automatically labeled samples will be further validated by the sample confidence, finally the cascade classifier can be updated by the online ensemble learning algorithm. The adaptability of the proposed method is improved by online adjusting the cascade classifier. Based on the detection results of video sequences, comparing with existing online ensemble learning methods, the detector trained by the proposed approach is adaptive and robust. It can adapt to features changes of the objects, detect objects in partial occlusion or cluttered background.

Key words: online learning, ensemble learning, object detection, Gentle AdaBoost algorithm