Intelligent traffic monitoring using video data mining

使用视频数据挖掘的智能交通监控

• 批准号: 566707-2021
• 负责人: Azim, Akramul
• 金额: $ 1.5万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=723629
• 关键词: Intelligent; traffic; monitoring; using; video

Intelligent traffic monitoring systems need to provide continuous service with appropriate adaptation in various dynamic situations. To ensure performance guarantees, we need to have sufficient knowledge of the system and its properties. A transportation system needs to have sufficient knowledge of its situations and its subsequent changes, which can be obtained from video streams. It is necessary for the system to have a prior knowledge of its interactions with various objects of the environment involving different timing constraints to ensure safe and correct operations. It is also essential for the system to extract timing properties by mining video data, and understand the flow of execution through creating a knowledge base. Our aim is to create a knowledge base called VideoMine from the monitored video stream containing traffic information of various objects. Our objectives also include that accessing information from VideoMine will consume significantly less memory, allow faster information processing, and help to identify, analyze and validate safety aspects.

智能交通监控系统需要在各种动态情况下提供持续的服务并进行适当的适应。为了确保性能保证，我们需要对系统及其属性有足够的了解。运输系统需要对其情况及其随后的变化有足够的了解，这可以从视频流中获得。系统有必要预先了解其与环境中涉及不同时间约束的各种对象的相互作用，以确保安全正确的操作。通过挖掘视频数据提取定时属性，并通过创建知识库来了解执行流程也是系统的关键。我们的目标是从监控的视频流中创建一个名为VideoMine的知识库，其中包含各种物体的交通信息。我们的目标还包括，从VideoMine访问信息将消耗更少的内存，允许更快的信息处理，并有助于识别、分析和验证安全方面。