Online tracking based on efficient transductive learning with sample matching costs

Visual tracking has been a popular and attractive topic in computer vision for a long time. In recent decades, many challenge problems in object tracking has been effectively resolved by using learning based tracking strategies. Number of investigations carried on learning theory found that when labeled samples are limited, the learning performance can be sufficiently improved by exploiting unlabeled ones. Therefore, one of the most important issue for semi-supervised learning is how to assign the labels to the unlabeled samples, which is also the principal focus of transductive learning. Unfortunately, considering the efficiency requirement of online tracking, the optimization scheme employed by the traditional transductive learning is hard to be applied to online tracking problems because of its large computational cost during sample labeling. In this paper, we proposed an efficient transductive learning for online tracking by utilizing the correspondences among the generated unlabeled and labeled samples. Those variational correspondences are modeled by a matching costs function to achieve more efficient learning of representative separators. With a strategy of fixed budget for support vectors, the proposed learning is updated by using a weighted accumulative average of model coefficients. We evaluated the proposed tracking on benchmark database, the experiment results have demonstrated an outstanding performance via comparing with the other state-of-the-art trackers.