# Robust video object tracking via Bayesian model averaging based feature fusion

arXiv: https://arxiv.org/abs/1604.00475 · HTML: https://mlsanity.com/paper/1604.00475/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2016.

## Built on by (most cited first, 2 of 2)

- ["Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us?](https://mlsanity.com/paper/2103.15294/) (2021)
- [Multiple feature fusion-based video face tracking for IoT big data](https://mlsanity.com/paper/2104.08096/) (2021)

## Abstract (arXiv)

In this article, we are concerned with tracking an object of interest in video stream. We propose an algorithm that is robust against occlusion, the presence of confusing colors, abrupt changes in the object feature space and changes in object size. We develop the algorithm within a Bayesian modeling framework. The state space model is used for capturing the temporal correlation in the sequence of frame images by modeling the underlying dynamics of the tracking system. The Bayesian model averaging (BMA) strategy is proposed for fusing multi-clue information in the observations. Any number of object features are allowed to be involved in the proposed framework. Every feature represents one source of information to be fused and is associated with an observation model. The state inference is performed by employing the particle filter methods. In comparison with related approaches, the BMA based tracker is shown to have robustness, expressivity, and comprehensibility.