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无基的语网络跟踪器,具有用于RGB-T跟踪的变压器
Liangsong Fan1,2, Pyeoungkee Kim3
1Silla University, 140, Baekyang-daero 700beon-gil, Sasang-gu, 46958, Busan, Korea.
Scientific reports
|August 16, 2023
概括
这项研究介绍了siamAFTS,一个无RGB-THERMAL跟踪网络. 它通过使用变压器模块和注意力机制来提高目标跟踪成功率,减少背景干扰.
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:
- 现有的RGB-THERMAL跟踪方法通常依赖于基于的算法和交叉相关性,限制了跟踪成功率.
- 挑战包括提高目标追踪精度和处理各种环境条件的困难.
研究的目的:
- 提出一个新的无追踪网络,siamAFTS,用于增强的RGB-THERMAL目标追踪.
- 通过提高目标追踪的成功率来解决当前方法的局限性.
主要方法:
- 开发了siamAFTS跟踪网络,一种无的方法,利用一个完全卷积网络和一个变压器模块.
- 集成通道和通道空间注意模块,以减轻背景干扰.
- 消除了点,以避免超参数调节和减少人类干预.
主要成果:
- 西安AFTS网络在三个基准数据集的目标跟踪中表现出更好的成功率.
- 拟议的模型有效地减少了背景干扰,从而导致更准确的界限框预测.
- 与现有的基于的追踪器 (如MANET,DAPNet,SGT和ADNet) 相比,实现了卓越的性能.
结论:
- 无 siamAFTS网络为RGB-THERMAL目标跟踪提供了显著的进步.
- 变压器和注意力模块的集成提高了跟踪的稳定性和准确性.
- 这种方法通过消除超参数依赖,简化了跟踪过程.
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