Research of multiple-instance learning for target recognition and tracking

Target recognition and tracking is a hot research in image and video processing and is widely used in motion analysis, behavior recognition, and so on. In this paper, we studied target recognition and tracking in a series of images, and our approach is based on the multiple-instance learning technique. Firstly, we present a general target tracking framework. Within the proposed framework, we use image frames to generate positive and negative samples to train a classifier and use the classifier to differentiate target from its background. We use a set of weak classifiers to construct a strong classifier. The experiments show that the proposed approach has better precision and recall on two public datasets than related works.