# Online Adaptive Hidden Markov Model for Multi-Tracker Fusion

arXiv: https://arxiv.org/abs/1504.06103 · HTML: https://mlsanity.com/paper/1504.06103/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2015.

## Built on by (most cited first, 4 of 4)

- [Is First Person Vision Challenging for Object Tracking?](https://mlsanity.com/paper/2108.13665/) (2021)
- [Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search](https://mlsanity.com/paper/2205.09676/) (2022)
- [CoCoLoT: Combining Complementary Trackers in Long-Term Visual Tracking](https://mlsanity.com/paper/2205.04261/) (2022)
- [Learning Spatial Distribution of Long-Term Trackers Scores](https://mlsanity.com/paper/2308.01256/) (2023)

## Abstract (arXiv)

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.