A novel online learning object detection system is proposed

which can self learning and improve its detection performance wihout human-annotated training data.The system is composed of a object detection module and a sample labeling module.Online fern classifier is used in the object detection module because of its fast online learning speed.Consequentely

our system can learn automatically and detect objects in the real time.Samples

which are used to train the classifier online

are acquired and labeled automatically from a two stages cascade method in the sample labeling module.Instead of training initial classifier from some manual labeled training samples like other online learning detection frameworks

our system can learn automatically after specifying the object to be detected.This can greatly reduce the efforts of labelers.Experimental results on several video datasets are provided to show the adaptive capability and high detection rate of our system.