Online co-training ranking SVM for visual tracking

Abstract

Online learned tracking is widely used to handle the appearance changes of object because of its adaptive ability. Learning to rank technique has attracted much attention recently in visual tracking. But the tracking method with online learning to rank suffers from the error accumulation problem during the self-training process. To solve this problem, we propose an online learning to rank algorithm in the co-training framework for robust visual tracking. A co-training algorithm combined with ranking SVM collects features and unlabeled data for training. Two ranking SVMs are built with different types of features accordingly and dynamically fused into a semi-supervised learning process. This semi-supervised learning approach is updated online to resist the occlusion and adapt to the changes of object's appearance. Many experiments on challenging sequences have shown that the proposed algorithm is more effective than the state-of-the-art methods.