Hierarchical Feature-Aware Tracking

TL;DR

This paper introduces a hierarchical feature-aware tracking framework that pre-selects experts based on past performance, improving efficiency and robustness in visual tracking compared to traditional post-event ensemble methods.

Contribution

It proposes a novel pre-event expert selection strategy that enhances efficiency, allows for more feature fusion, and reduces overfitting in ensemble visual tracking.

Findings

Achieves state-of-the-art performance on public datasets.

Demonstrates improved robustness and efficiency over existing ensemble trackers.

Effectively mitigates overfitting through adaptive expert selection.

Abstract

In this paper, we propose a hierarchical feature-aware tracking framework for efficient visual tracking. Recent years, ensembled trackers which combine multiple component trackers have achieved impressive performance. In ensembled trackers, the decision of results is usually a post-event process, i.e., tracking result for each tracker is first obtained and then the suitable one is selected according to result ensemble. In this paper, we propose a pre-event method. We construct an expert pool with each expert being one set of features. For each frame, several experts are first selected in the pool according to their past performance and then they are used to predict the object. The selection rate of each expert in the pool is then updated and tracking result is obtained according to result ensemble. We propose a novel pre-known expert-adaptive selection strategy. Since the process is more efficient, more experts can be constructed by fusing more types of features which leads to more robustness. Moreover, with the novel expert selection strategy, overfitting caused by fixed experts for each frame can be mitigated. Experiments on several public available datasets demonstrate the superiority of the proposed method and its state-of-the-art performance among ensembled trackers.