变压器增强的双分支姆追踪器,具有自信感知回归和自适应模板更新
K S Sachin Sakthi1, Jae Hoon Jeong2, Woo Young Choi3
1Department of Control and Instrumentation Engineering, Pukyong National University, 45 Yongso-ro, Busan, 48513, South Korea.
Scientific reports
|January 13, 2026
概括
TSDTrack是一个新的变压器增强的语追踪器,通过整合全球关注和质量意识预测来增强视觉对象跟踪. 这种方法提高了对遮和外观变化的稳定性,实现了最先进的结果.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 罗网络对于视觉对象跟踪是有效的,但与静态模板,有限的上下文和功能集成扎,特别是在封闭或杂乱时.
- 由于背景变化和目标外观变化,现有的方法在保持跟踪准确性方面面临挑战.
研究的目的:
- 开发一款强大且具有质量意识的视觉对象追踪器,克服传统姆网络的局限性.
- 通过基于变压器的功能融合和自适应模板更新来提高跟踪性能.
主要方法:
- 提出了TSDTrack,这是一个用变压器增强的语跟踪器,利用ResNet骨干进行多级特征提取.
- 实现了一个具有全球关注的变压器模块,以提高语义和空间一致性.
- 引入了信任意识分支 (CAB) 和回归分布学习 (RDL) 进行精确的本地化和质量评估.
- 开发了一个可靠模板更新策略,用于自适应外观建模.
主要成果:
- 在基准数据集上,TSDTrack实现了最先进的性能:LaSOT (55.5%的成功率),GOT-10k (67.5%的AO),OTB100 (71.6%的AUC) 和UAV123 (66.4%的成功率).
- 与最近的基于变压器和姆追踪器相比,追踪器表现出卓越的准确性和稳定性.
- 自信意识和分布学习分支在不确定性下提高了本地化精度.
结论:
- TSDTrack在视觉对象跟踪方面取得了重大进展,特别是在具有挑战性的场景中.
- 变压器和质量意识机制的整合为自适应跟踪提供了一个强大的框架.
- 拟议的方法有效地解决了姆追踪器上下文建模和功能集成方面的局限性.
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