Intelligent traffic monitoring using video data mining

智能交通监控系统需要在各种动态情况下提供适当适应的连续服务。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。交通系统需要充分了解它的情况和随后的变化，这些信息可以从视频流中获得。系统必须事先了解其与涉及不同定时约束的环境中的各种对象的交互，以确保安全和正确的操作。通过挖掘视频数据来提取时序属性，通过创建知识库来理解执行流程也是系统必不可少的。我们的目标是从监控到的包含各种对象的交通信息的视频流中创建一个名为Videomine的知识库。我们的目标还包括，从Videomine访问信息将大大减少内存消耗，允许更快的信息处理，并有助于识别、分析和验证安全方面。

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.