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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 informationdoi:10.1109/icip.2013.6738805 dblp:conf/icip/LiuXA13 fatcat:ckaulla6kfcdde7eiqfb7os2tm