A novel online learning method for head detection in video sequences

Abstract

Online learning is an effective incremental learning method. Compared with the conventional off-line learning method, online learning updates the original classifier continuously with new samples and improves its performance. In this paper, we propose a novel online learning framework for head detection in video sequences. At first, an off-line classifier is trained with a few labeled samples. And it was used to object detection in video sequences. Based on online boosting algorithm, the detected objects will be used to train the classifier as new samples. Instead of using another detection algorithm to label the new sample automatically like other online learning framework, we ensure the correct label from tracking. Furthermore, the weights of new samples can be obtained from tracking directly. Thus the training speed of the classifier can be improved. Experimental results on two video datasets are provided to show the efficient and high detection rate of the framework.