相关实验视频
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多对象跟踪与基于信心的轨迹预测方案
Kai Yi1, Jiarong Li2, Yi Zhang2
1Intelligent Policing Key Laboratory of Sichuan Province, Luzhou 646000, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
这项研究介绍了ConfMOT,一种新的多对象跟踪方法,通过使用检测置信度得分来增强卡尔曼波器,以管理噪音和改善轨迹预测,显著减少拥挤场景中的身份开关.
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:
- 多对象跟踪 (MOT) 通过视频将对象关联起来,以保持稳定的轨迹.
- 当前的MOT方法通常根据信心分数过检测,忽视检测结果的充分利用.
- 卡尔曼波器 (KF) 在MOT中广泛用于序列处理,但在拥挤的场景中与噪音作斗争,导致身份开关 (IDS) 和跟踪失败.
研究的目的:
- 调查多对象跟踪 (MOT) 中现有的轨迹预测方案的局限性.
- 为了证明卡尔曼波器 (KF) 可以在适当的噪音处理下在视频序列处理中获得竞争力的结果.
- 提出一种新的基于信心的轨迹预测方案 (ConfMOT),以提高MOT的性能.
主要方法:
- 提出了一个基于信心的轨迹预测方案 (ConfMOT),利用卡尔曼波器 (KF).
- 使用检测信心分数 (CS) 来调整更新期间的KF噪声,并预测对象轨迹.
- 实施成本矩阵 (CM) 来匹配不可靠的对象,并删除基于CS的丢失轨迹.
主要成果:
- 与主流数据集上的先进竞争对手相比,ConfMOT跟踪器表现出更高的性能.
- 拟议的方法有效地处理拥挤场景中的噪音,减少身份开关 (IDS) 和跟踪故障.
- ConfMOT被证明是一个简单而有效的跟踪解决方案.
结论:
- 卡尔曼波器 (KF) 仍然是一个可行的和有竞争力的多对象跟踪 (MOT) 的方法,当噪声得到适当的管理.
- 基于信心的轨迹预测方案 (ConfMOT) 在跟踪准确性和稳定性方面提供了显著的改进.
- ConfMOT为多对象跟踪挑战提供了高效和有效的解决方案,特别是在复杂的环境中.
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