MACTrack: 时空背景传播与运动补偿用于反无人机跟踪
Wanying Xu1, Haijiang Sun2, Shuai Wang2
1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China; University of Chinese Academy of Sciences, Beijing, 101408, China.
MACTrack通过整合运动意识和上下文建模来增强反无人飞行器 (UAV) 追踪. 这种强大的框架在复杂的场景中提高了目标本地化和稳定性,优于现有的方法.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 未经授权的无人机 (UAV) 活动对安全构成重大威胁.
- 现有的反无人机视觉跟踪系统与敏捷的运动,复杂的背景和平台-无人机运动合作斗争,导致跟踪漂移.
研究的目的:
- 开发一个强大的反无人机追踪框架,解决当前方法的局限性.
- 在具有挑战性的反无人机场景中提高目标定位精度和跟踪稳定性.
主要方法:
- 拟议的MACTrack框架将运动意识与时空背景建模相结合.
- 不对称的空间上下文模块利用自我注意和交叉注意来增强歧视.
- 时间上下文传播模块具有内存引导的注意力,用于自适应的历史信息聚合.
- 运动补偿模块用于对齐特征表示,以减轻平台运动效应.
主要成果:
- 在ANTI-UAV410基准测试中,MACTrack实现了88.9%的准确率和66.5%的成功率.
- 在准确度上表现比竞争基线高7.9%,成功率高7.4%.
- 在反无人机追踪中,在处理性能下降方面取得了显著的改进.
结论:
- 拟议的MACTrack框架有效地解决了反无人机跟踪性能下降的问题.
- 运动意识和时空环境建模的联合集成对于强大的无人机跟踪至关重要.
- MACTrack提供了一个有前途的解决方案,用于加强对未经授权的无人机活动的安全性.
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