Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

376
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 of...
376
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.2K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Deep inception neural network with residual connections for Tamil handwritten character recognition.

Scientific reports·2026
Same author

Dynamic SG-SKRDX hybrid framework for precision weather forecasting and crop suitability in the Cauvery Delta.

Scientific reports·2025
Same author

A custom hash algorithm for hosting secure gray scale image repository in public cloud.

Scientific reports·2025
Same author

An explainable machine learning (XAI) framework to enhance types of cardiovascular disease diagnosis and prognosis.

Physical and engineering sciences in medicine·2025
Same author

Analysis of hybrid integer wavelet transform and singular value decomposition for image steganography under various noise conditions.

Scientific reports·2025
Same author

Convolutional neural network and wavelet composite against geometric attacks a watermarking approach.

Scientific reports·2025

相关实验视频

Updated: Jan 11, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.5K

开发基于游戏理论的超图的自动编码方案,用于监控视频的多个对象跟踪和异常检测.

Palanivel Srinivasan1, Karthik Mohan2, Manivannan Doraipandian3

  • 1Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, 201 204, India.

Scientific reports
|November 17, 2025
PubMed
概括

这项研究引入了监控中异常检测的统一框架,将游戏理论超图匹配 (GTHG) 与卷积自编码器 (CAE) 集成在一起. 这种新的方法提高了在对象跟踪期间识别可疑活动的准确性和效率.

关键词:
自动编码器自动编码器游戏理论的游戏理论.超图形 (Hypergraph) 是一个超图形.坚固的功能 强大的功能视频异常检测检测 视频异常检测

更多相关视频

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K

相关实验视频

Last Updated: Jan 11, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.5K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K

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:

  • 在监视中检测异常对于安全和保障至关重要.
  • 基于图形和超图形 (HG) 的方法有助于对象跟踪以检测异常.
  • 现有的方法难以在同时跟踪和检测中平衡准确性和效率.

研究的目的:

  • 提出一个统一的框架,用于监控录像中检测异常.
  • 将游戏理论超图匹配 (GTHG) 与卷积自编码器 (CAE) 集成在一起.
  • 通过结合结构一致性和外观重建来提高检测准确性和计算性能.

主要方法:

  • 开发了一个统一的框架,整合了GTHG和CAE.
  • 结合结构一致性和外观重建,用于异常检测.
  • 在基准视频数据集上测试了该方法,分析了对匹配.

主要成果:

  • 实现了高的曲线下面积 (AUC) 分数:88.7% (UCSD Ped1),91.2% (UCSD Ped2) 和86.6% (CUHK大道).
  • 与现有的异常检测模型相比,其表现优越.
  • 验证了综合方法在提高准确性和效率方面的有效性.

结论:

  • 拟议的GTHG和CAE综合框架在异常检测方面取得了重大进展.
  • 这种统一的方法有效地解决了单独的跟踪和检测方法的局限性.
  • 该框架为加强监控安全和安全标准提供了强有力的解决方案.