Unique framework for video segmentation, and categorization applicable to traffic and medical environments

适用于交通和医疗环境的视频分割和分类的独特框架

• 批准号: RGPIN-2015-04588
• 负责人: Wu, Jonathan
• 金额: $ 3.42万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=613050
• 关键词: Unique; framework; video; segmentation; categorization

Since the advent of digital media, it has been utilized in numerous ways to automate manual work and bring about efficiencies in different application areas. Video processing, among other media, has been one of the most sought-after technologies due to its power of representation. Videos are used in advertising, surveillance, traffic regulation, and entertainment virtually everywhere in the world. Thus, automatic processing of visual data has also seen increased popularity in research and technology. The main target of this project is in the sector of scene understanding, which is a central element of automatic video processing. Scene understanding refers to the automatic detection and identification of the objects present in videos with the aim of annotating videos with corresponding search tags. Automatic scene understanding has been applied in several sectors, namely traffic monitoring, surveillance, context-based video retrieval, video categorization, and medical video analysis. The segmentation of meaningful objects in videos is the step prior to the process of scene understanding. Therefore, we are proposing a unique framework for video segmentation and categorization for scene understanding. Considerable background knowledge and expertise is required to contribute to and improve upon existing work in this area. Through years of background development and research, we have concluded that the framework needs to be developed by dividing the target into distinct tasks to achieve a number of short-term objectives, holistically representing the final long-term objective of constructing a framework for scene understanding. We estimated nine (9) dedicated HQPs to be appointed for the accomplishment of each task individually, with their efforts culminating in achieving the final goal. The short-term objective is to design the framework, verify and validate it for different environments. The long-term objective is to specifically target the application areas of traffic and medical environments since these two areas have major impact on public safety and health. This novel framework would also establish a stepping stone towards more general scene understanding.

自从数字媒体出现以来，它已经以多种方式被用于自动化手工工作，并在不同的应用领域提高效率。在其他媒体中，视频处理由于其表现能力一直是最受欢迎的技术之一。视频几乎在世界各地被用于广告、监视、交通管制和娱乐。因此，视觉数据的自动处理在研究和技术上也越来越受欢迎。这个项目的主要目标是场景理解领域，这是自动视频处理的核心要素。