Learning Structure Models with Context Information for Visual Tracking

Tracking objects that undergo abrupt appearance changes and heavy occlusions is a challenging problem which conventional tracking methods can barely handle. To address the problem, we propose an online structure learning algorithm that contains three layers： an object is represented by a mixture of online structure models （OSMs） which are learnt from block-based online random forest classifiers （BORFs）. BORFs are able ~o handle occlusion problems since they model local appearances of the target. To further improve the tracking accuracy and reliability, the algorithm utilizes mixture relational models （MRMs） as multi-mode context information to integrate BORFs into OSMs. Furthermore, the mixture construction of OSMs can avoid over-fitting effectively and is more flexible to describe targets. Fusing BORFs with MRMs, OSMs capture the discriminative parts of the target, which guarantees the reliability and robustness of our tracker. In addition, OSMs incorporate with block occlusion reasoning to update our BORFs and MRMs, which can deal with appearance changes and drifting problems effectively. Experiments on challenging videos show that the proposed tracker performs better than several state-of-the-art algorithms.