Content-based video analysis for visual surveillance and video interpretation

用于视觉监控和视频解释的基于内容的视频分析

• 批准号: 184103-2011
• 负责人: Laganière, Robert
• 金额: $ 1.6万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=523038
• 关键词: Content; based; video; analysis; visual

The objective of this research is to develop new methods and algorithms for the analysis of video content. Our goal is to extract meaningful information from live video streams and from video recordings. The extracted information could be about the scene content and its structure or about the actions that are occurring. In particular, we will develop approaches to be used in the context of intelligent visual surveillance where events of interest must be reliably detected and classified. We will study the problem of the reliable long-term tracking of moving objects, under occlusions, and across cameras. Our strategy will be to build an environmental model of the observed scene containing structural information about the static elements and representation of the way the moving objects typically interact with the scene. We also want to develop approaches to improve the automatic classification of events and objects, partly using the user's feedback in order to more accurately identified the most relevant events and therefore take the most appropriate action upon detection of such event.

这项研究的目的是开发新的方法和算法来分析视频内容。我们的目标是从实时视频流和视频记录中提取有意义的信息。所提取的信息可以是关于场景内容及其结构或关于正在发生的动作的。特别是，我们将开发用于智能视觉监视的方法，在这种情况下，必须可靠地检测和分类感兴趣的事件。我们将研究在遮挡和摄像机之间对运动目标进行可靠的长期跟踪的问题。我们的策略将是建立一个观察到的场景的环境模型，其中包含关于静态元素的结构信息，以及移动对象通常与场景交互的方式的表示。我们还希望开发改进事件和对象的自动分类的方法，部分使用用户的反馈，以便更准确地识别最相关的事件，从而在检测到此类事件时采取最适当的行动。