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相关概念视频

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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相关实验视频

Updated: Sep 10, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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具有变压器和注意力机制的双阶段多对象跟踪算法

Mingxing Hou1, Yiming Wu1, Hong Shi1

  • 1School of Computer Science and Technology, Taiyuan Normal University, Taiyuan, 030000, China.

Scientific reports
|August 26, 2025
PubMed
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.

关键词:
注意机制多对象跟踪重新识别两个阶段的关联你只看一次版本8

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Last Updated: Sep 10, 2025

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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.