通过时空传播和模式协同作用实现统一的多模式对象跟踪
Jiajia Wu1,2,3, Haorui Zuo1,2,3, Yuxing Wei1,2,3
1State Key Laboratory of Optical Field Manipulation Science and Technology, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China.
Journal of imaging
|December 24, 2025
概括
通过统一任务和改进数据融合,SMUTrack增强了多模式对象跟踪 (MMOT). 这个框架克服了异质数据和动态场景的挑战,以获得强大的感知.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 多模式对象跟踪 (MMOT) 利用多个传感器来克服单传感器系统的局限性.
- 现有的MMOT方法在复杂,动态环境中扎着数据异质性,模式间不平衡和降低可靠性.
研究的目的:
- 提出SMUTrack,一个统一的多模式对象跟踪框架,它解决了代表性学习和泛化的挑战.
- 在动态场景中增强多模式一致的感知和数据聚合.
主要方法:
- 开发了SMUTrack,这是一个统一的框架,具有全球共享参数,整合了三个下游MMOT任务.
- 实施了批量合并和分割策略,并进行多任务联合培训,以建立跨模式的相关性.
- 引入了一个层次模式协同和加强 (HMSR) 模块和一个封闭的融合和上下文意识 (GFCA) 模块,用于逐步的信息交换.
- 整合了一个时空信息传播 (SIP) 机制,以学习背景关系的轨迹和外观线索.
主要成果:
- 在主流MMOT数据集上,SMUTrack表现出色.
- 该框架在各种MMOT任务中表现出强大的适应性.
- 拟议的模块有效地提高了多模式表示和跟踪稳定性.
结论:
- SMUTrack为多模式对象跟踪提供了统一有效的解决方案.
- 该框架成功地解决了数据异质性和动态环境的挑战.
- 对于先进的MMOT应用,SMUTrack提供了一种强大而可适应的方法.
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