Online structure learning for robust object tracking

In this paper, we aim to track objects that undergo abrupt appearance changes and heavy occlusions. To address these problems, we propose an online structure learning algorithm which contains two layers, block-based online random forest classifiers (BORFs) and online structure models (OSMs). BORFs are able to handle occlusion problems since they model local appearances of the target. To further improve the accuracy and reliability, the algorithm utilizes relational models as context information to combine BORFs into the online structure models. Capturing the discriminative parts of targets with online learnt structures, OSMs help locate targets accurately even when they are heavily occluded. In addition, OSMs guide the block occlusion reasoning and the update scheme of BORFs and relational models, which can handle appearance changes and drift problems effectively. Experiments on challenging videos show that the proposed tracker performs better than several state-of-the-art algorithms which demonstrate the effectiveness of our approach.