Boundary Effect-Aware Visual Tracking for UAV with Online Enhanced Background Learning and Multi-Frame Consensus Verification

TL;DR

This paper introduces a boundary effect-aware visual tracker for UAVs that leverages online background learning and multi-frame verification to improve robustness and accuracy in object tracking.

Contribution

It proposes a novel tracker combining online background learning, spatial penalization, convolutional features, and multi-frame verification to address boundary effects and appearance changes.

Findings

Achieved state-of-the-art performance on 100 UAV sequences.

Effectively mitigated boundary effects and improved tracking robustness.

Enhanced object representation with convolutional features.

Abstract

Due to implicitly introduced periodic shifting of limited searching area, visual object tracking using correlation filters often has to confront undesired boundary effect. As boundary effect severely degrade the quality of object model, it has made it a challenging task for unmanned aerial vehicles (UAV) to perform robust and accurate object following. Traditional hand-crafted features are also not precise and robust enough to describe the object in the viewing point of UAV. In this work, a novel tracker with online enhanced background learning is specifically proposed to tackle boundary effects. Real background samples are densely extracted to learn as well as update correlation filters. Spatial penalization is introduced to offset the noise introduced by exceedingly more background information so that a more accurate appearance model can be established. Meanwhile, convolutional features are extracted to provide a more comprehensive representation of the object. In order to mitigate changes of objects' appearances, multi-frame technique is applied to learn an ideal response map and verify the generated one in each frame. Exhaustive experiments were conducted on 100 challenging UAV image sequences and the proposed tracker has achieved state-of-the-art performance.