Multiple instance tracking based on hierarchical maximizing bag´s margin boosting

In online tracking, the tracker evolves to reflect variations in object appearance and surroundings. This updating process is formulated as a supervised learning problem, thus a slight inaccuracy of the tracker will degrade the updating. Multiple Instance Learning (MIL) is used to alleviate such a problem by representing training samples in bags of image patches (or called instances). Difficulties are then passed on to the learning method to train a classifier that discovers the most accurate instance. This paper proposes a Maximizing Bag´s Margin (MBM) criteria for MIL. Combined with MBM, a hierarchical boosting is proposed for updating, in which bag and instance weights are introduced to guide classifier retrain ing. Our approach effectively improves the updating´s efficiency with less computation cost. Experiments demonstrate the benefits of our method.