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<p>Discriminative correlation filters have shown promising results in object tracking by using spatial and temporal regularization to reduce boundary effects and guide model updates. However, traditional approaches often apply uniform regularization across multi-channel features, limiting their adaptability to appearance changes and complex backgrounds. This study introduces the surrounding sparse spatial attribute-aware correlation filter (SSSACF) to address these challenges. The method incorporates attribute-aware sparsity to extract meaningful information from multi-channel attribute patterns, enhancing object discrimination. A novel ranking mechanism samples adjacent patches around the target, integrating contextual information for improved localization and continuous tracking in dynamic scenes. Furthermore, a sparse spatial integration method effectively suppresses adverse background influences and boundary effects using reference weights. The proposed SSSACF tracker achieves robust and efficient tracking, significantly outperforming state-of-the-art methods on publicly available datasets, demonstrating its effectiveness in addressing the limitations of traditional correlation filters.</p>

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Learning sparse spatial attribute-aware correlation filter tracking via rank-based surrounding strategy

  • 期刊论文

  • 发表日期：

    2025年7月15日

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  • Dinesh Elayaperumal,
  • Sachin Sakthi Kuppusami Sakthivel,
  • Young Hoon Joo,
  • Jae Hoon Jeong

摘要

Discriminative correlation filters have shown promising results in object tracking by using spatial and temporal regularization to reduce boundary effects and guide model updates. However, traditional approaches often apply uniform regularization across multi-channel features, limiting their adaptability to appearance changes and complex backgrounds. This study introduces the surrounding sparse spatial attribute-aware correlation filter (SSSACF) to address these challenges. The method incorporates attribute-aware sparsity to extract meaningful information from multi-channel attribute patterns, enhancing object discrimination. A novel ranking mechanism samples adjacent patches around the target, integrating contextual information for improved localization and continuous tracking in dynamic scenes. Furthermore, a sparse spatial integration method effectively suppresses adverse background influences and boundary effects using reference weights. The proposed SSSACF tracker achieves robust and efficient tracking, significantly outperforming state-of-the-art methods on publicly available datasets, demonstrating its effectiveness in addressing the limitations of traditional correlation filters.