ORT:用于多个对象的跟踪,具有阻塞强度
Shoudong Han1, Hongwei Wang1, En Yu1
1National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
Fundamental research
|June 18, 2025
概括
这项研究引入了一个新的多对象跟踪 (MOT) 框架,以解决长期的闭塞问题. 它使用新的预测和关联模块来提高轨迹的准确性和纯度,而无需外观数据.
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:
- 多对象跟踪 (MOT) 与长期的遮蔽作斗争,在这种情况下,外观特征变得不可靠.
- 像卡尔曼波器这样的现有方法在闭塞期间的长期运动预测中失败.
- 在MOT中的数据关联容易发生身份切换,因为在遮蔽期间的得分相似.
研究的目的:
- 开发一个强大的MOT框架,有效地处理长期的对象封闭.
- 改进轨迹重新连接,减少隐蔽场景中的身份切换.
- 为了增强隐蔽对象的运动预测和数据关联.
主要方法:
- 提出了使用空间和时间变压器进行状态估计和可见性预测的长期空间时间预测 (LSTP) 模块.
- 引入了一个有序关联 (OA) 模块,该模块根据封闭属性和活跃状态优先考虑轨迹关联.
- 开发了一个包含LSTP和OA模块的Occlusion-Robust Tracker (ORT) 框架.
主要成果:
- ORT框架显著缓解了多对象跟踪中的长期封闭问题.
- 在MOT17和MOT20基准上取得了竞争性表现,超过了最先进的追踪器.
- 证明了卓越的轨迹准确性和纯度,即使不依赖外观信息.
结论:
- 拟议的LSTP和OA模块有效地解决了阻塞所带来的运动预测和数据关联挑战.
- 该ORT框架提供了一个强大的解决方案,用于多对象跟踪在具有显著阻塞的场景.
- 这种方法通过在具有挑战性的条件下提高跟踪稳定性和可靠性来提高MOT能力.
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