Online Adaptive Hidden Markov Model for Multi-Tracker Fusion
This addresses the challenge of robust object tracking in computer vision, offering a novel fusion approach that improves accuracy and reliability.
The paper tackles the problem of visual object tracking by proposing HMMTxD, a method that fuses multiple trackers and a detector using an online adaptive hidden Markov model, and it outperforms state-of-the-art methods on standard benchmarks and a large dataset of 77 sequences.
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.