Adaptive Multi-Feature Reliability Re-Determinative Correlation Filter for Visual Tracking

doi:10.1109/TMM.2020.3032043

2021-01-01

PRE

Mingyang Guan *

Changyun Wen

DOI：10.1109/TMM.2020.3032043

原文链接

原文求助

摘要

En 中文

Model drift is a challenging issue for visual object tracking. Most available approaches aim to address this issue by constructing of a stronger discriminative prediction model with the integration of multiple features. In this article, we also address the issue of model drift using multiple features, but we consider the fusion problem from a different point of view, namely, strengthening the features that are suitable for the current scenario while weakening the remaining ones. Therefore, an advanced tracker that adaptively redetermines the reliability for each feature during the tracking process is proposed. Furthermore, to correctly evaluate and redetermine these reliabilities, two different solutions, called model evaluation and numerical optimization, are proposed, and two independent trackers corresponding to these two solutions are implemented. Extensive experiments have been designed on five large datasets to validate the following: 1) The proposed tracking framework is superior for making the tracking model more robust, and 2) the two solutions proposed for redetermining the reliability for each feature are effective. As expected, the two implemented trackers do indeed improve the accuracy and robustness compared to state-of-the-art trackers. Especially on VOT2016, the proposed trackers based on model evaluation and numerical optimization achieve outstanding EAO scores (i.e., 0.453 and 0.428, respectively), outperforming the recently developed top trackers by a large margin.

Keyword:

Target tracking

Reliability

Visualization

Adaptation models

Numerical models

Computational modeling

Hidden Markov models

Correlation filter

machine learning

multiple features

visual tracking