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<p>Existing object trackers commonly approach the tracking process by utilizing classification and regression techniques. However, they often encounter difficulties in managing complex scenarios such as occlusions and appearance changes. Moreover, the quality of candidate boxes is a critical factor for affecting the tracking performance. To overcome these challenges, this study introduces a border-aware tracking framework based on a vision transformer (ViT), termed ViT-BF. Through the integration of a boundary alignment operation, ViT-BF extracts boundary features from the extremal points of objects, thereby enhancing classification and regression precision. To handle the dynamic appearance variations of objects, ViT-BF integrates a template update mechanism through a score prediction module (SPM), which enhances the tracker’s robustness and accuracy. Experimental results reveal that ViT-BF demonstrates excellent performance across multiple benchmarks, including an AUC score of 85.0% on TrackingNet, 89.4% in normalized precision, and 84.4% in precision. The promising results extend to other standard datasets such as LaSOT, GOT-10k, and UAV123, validating our method’s strong stability and adaptability in diverse tracking scenarios.</p>

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ViT-BF: vision transformer with border-aware features for visual tracking

  • 期刊论文

  • 发表日期：

    2025年5月25日

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  • Kai Yang,
  • Wenhao Zhang,
  • Ping Li,
  • Jinxing Liang,
  • Tao Peng,
  • Jia Chen,
  • Li Li,
  • Xinrong Hu,
  • Junping Liu

摘要

Existing object trackers commonly approach the tracking process by utilizing classification and regression techniques. However, they often encounter difficulties in managing complex scenarios such as occlusions and appearance changes. Moreover, the quality of candidate boxes is a critical factor for affecting the tracking performance. To overcome these challenges, this study introduces a border-aware tracking framework based on a vision transformer (ViT), termed ViT-BF. Through the integration of a boundary alignment operation, ViT-BF extracts boundary features from the extremal points of objects, thereby enhancing classification and regression precision. To handle the dynamic appearance variations of objects, ViT-BF integrates a template update mechanism through a score prediction module (SPM), which enhances the tracker’s robustness and accuracy. Experimental results reveal that ViT-BF demonstrates excellent performance across multiple benchmarks, including an AUC score of 85.0% on TrackingNet, 89.4% in normalized precision, and 84.4% in precision. The promising results extend to other standard datasets such as LaSOT, GOT-10k, and UAV123, validating our method’s strong stability and adaptability in diverse tracking scenarios.