Higher Performance Visual Tracking with Dual-Modal Localization

TL;DR

This paper introduces a dual-modal localization framework for visual object tracking that balances robustness and accuracy by combining online regression and offline classification, achieving state-of-the-art results.

Contribution

The work proposes a novel dual-modal ensemble framework for target localization in visual tracking, effectively balancing robustness and accuracy.

Findings

Achieves state-of-the-art performance on 8 benchmark datasets.

Effectively balances robustness and accuracy in visual tracking.

Outperforms existing methods in diverse tracking scenarios.

Abstract

Visual Object Tracking (VOT) has synchronous needs for both robustness and accuracy. While most existing works fail to operate simultaneously on both, we investigate in this work the problem of conflicting performance between accuracy and robustness. We first conduct a systematic comparison among existing methods and analyze their restrictions in terms of accuracy and robustness. Specifically, 4 formulations-offline classification (OFC), offline regression (OFR), online classification (ONC), and online regression (ONR)-are considered, categorized by the existence of online update and the types of supervision signal. To account for the problem, we resort to the idea of ensemble and propose a dual-modal framework for target localization, consisting of robust localization suppressing distractors via ONR and the accurate localization attending to the target center precisely via OFC. To yield a final representation (i.e, bounding box), we propose a simple but effective score voting strategy to involve adjacent predictions such that the final representation does not commit to a single location. Operating beyond the real-time demand, our proposed method is further validated on 8 datasets-VOT2018, VOT2019, OTB2015, NFS, UAV123, LaSOT, TrackingNet, and GOT-10k, achieving state-of-the-art performance.