具有变压器和注意力机制的双阶段多对象跟踪算法
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在PubMed上查看摘要
概括
此摘要是机器生成的。本研究引入了使用增强的YOLOv8和ByteTrack来解决工程安全中的封闭和身份交换机的改进多对象跟踪模型. 该模型在行人跟踪方面实现了高精度,通过减少错误和提高稳定性来提高安全性.
科学领域
- 工程安全
- 计算机视觉
- 人工智能
背景情况
- 多对象跟踪在工程安全应用中面临着阻塞和频繁的身份开关 (ID) 的挑战.
- 现有的追踪通过检测模型在具有挑战性的条件下难以准确检测和保持目标身份.
研究的目的
- 提出一个先进的多对象跟踪模型,集成改进的You Only Look Once版本8 (YOLOv8) 和通过跟踪字节 (ByteTrack) 的高性能多对象跟踪.
- 增强对象检测和重新识别能力,以减轻遮蔽问题,减少识别开关,以提高追踪稳定性.
主要方法
- 开发了一种新的协调注意力空间金字塔聚合 - 快速传输 (CASPPFC) 模块,并将其与改进的高效视觉变压器 (EfficientViT) 集成,以增强YOLOv8骨干.
- 引入全方位网络坐标注意 (OSNet-CA) 进行有效的重新识别 (Re-ID) 功能提取.
- 在关联阶段采用有效交叉对欧盟 (EIoU) 方法来完善目标位置关系.
主要成果
- 改进的模型在MOT17数据集中实现了80.5%的MOTA,79.3%的IDF1和64.2%的HOTA.
- 在MOT20数据集中,该模型达到77.8%的MOTA,76.9%的IDF1和62.4%的HOTA.
- 与基线方法相比,虚假阳性,虚假阴性和身份切换显著减少.
结论
- 拟议的多对象跟踪模型有效地解决了工程安全中的封闭和ID开关挑战.
- 集成了CASPPFC,EfficientViT,OSNet-CA和EIoU等先进的模块,大大提高了跟踪的准确性和稳定性.
- 该模型提供高精度的行人跟踪,对于及时检测危险事件和确保工程环境中的人员安全至关重要.
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