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通过基于查询和模板匹配的基于变压器的多个对象跟踪.

Qinyu Wang1, Chenxu Lu1, Long Gao1

  • 1State Key Laboratory of Integrated Service Networks, School of Telecommunications Engineering, Xidian University, No. 2, South Taibai Street, Hi-Tech Development Zone, Xi'an 710071, China.

Sensors (Basel, Switzerland)
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PubMed
概括
此摘要是机器生成的。

基于的查询 (ABQ) 和模板匹配 (TM) 模块加速了多个对象跟踪 (MOT) 的联合检测和跟踪 (JDT). 与现有方法相比,ABQ-Track实现了更快的融合和更好的性能.

关键词:
基于的查询查询多个对象跟踪跟踪.模板匹配的匹配方式变压器的变压器是一个变压器.视频处理视频处理

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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:

  • 多重对象跟踪 (MOT) 对于智能视频分析至关重要,联合检测和跟踪 (JDT) 方法提供了统一的方法.
  • 目前的JDT方法在训练速度和数据关联准确性方面存在局限性,这阻碍了它们的整体性能.

研究的目的:

  • 提高MOT的JDT方法的效率和有效性.
  • 引入新的模块,以加快培训的融合和改进MOT中的数据关联.

主要方法:

  • 提出了基于的查询 (ABQ) 模块,该模块将框坐标集成到可学习的查询中,为专注的特征学习提供明确的空间先验.
  • 开发了一个模板匹配 (TM) 模块,使 JDT 方法能够将当前检测与历史轨迹特征联系起来.
  • 推出了ABQ-Track,一种基于变压器的新型MOT方法,包括ABQ和TM模块.

主要成果:

  • 与基线方法相比,ABQ模块显著加快了培训趋同,将培训时代从150个缩短到50个.
  • TM模块通过利用历史特征信息来增强数据关联能力.
  • 在广泛的实验中,ABQ-Track在现有的JDT方法 (如TransTrack) 上表现出优越的性能.

结论:

  • 拟议的ABQ和TM模块有效地解决了MOT中现有的JDT方法的局限性.
  • 对于多个对象跟踪任务,ABQ-Track提供了更有效,更准确的解决方案.
  • 空间先验和历史特征匹配的整合代表了JDT方法学的重大进步.