  In this paper, we propose a novel method for visual object tracking called
HMMTxD. The method fuses observations from complementary out-of-the box
trackers and a detector by utilizing a hidden Markov model whose latent states
correspond to a binary vector expressing the failure of individual trackers.
The Markov model is trained in an unsupervised way, relying on an online
learned detector to provide a source of tracker-independent information for a
modified Baum- Welch algorithm that updates the model w.r.t. the partially
annotated data.
  We show the effectiveness of the proposed method on combination of two and
three tracking algorithms. The performance of HMMTxD is evaluated on two
standard benchmarks (CVPR2013 and VOT) and on a rich collection of 77 publicly
available sequences. The HMMTxD outperforms the state-of-the-art, often
significantly, on all datasets in almost all criteria.
