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 retraining. Our approach effectively improves the updating's efficiency with less computation cost. Experiments demonstrate the benefits of our method.