Pedestrian detection and segmentation in videos captured by cameras exhibiting large, uncontrolled motion

摄像机拍摄的视频中的行人检测和分割呈现出较大的、不受控制的运动

• 批准号: 486268-2015
• 负责人: Qureshi, Faisal
• 金额: $ 1.82万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=585489
• 关键词: Pedestrian; detection; segmentation; videos; captured

Video surveillance is increasingly being used to record police-citizenry interactions to increase officer safety and to curb police brutality. Many police services, both in Canada and internationally, have started pilot projects where officers will be equipped with body worn cameras that will record their surroundings as they respond to events. Body worn cameras raise serious privacy concerns. Police departments, for example, often have to release the videos in response to a freedom of information request. The police departments are required by law to hide or mask the identities of non-actors before these videos are released. This is to preserve the privacy of innocent individuals seen in the videos. Existing video redaction or masking tools are time consuming and laborious. These are designed to deal with videos captured from stationary cameras. Body worn cameras undergo large, uncontrolled motions, and we need new tools and theory to develop automated tools for detecting and masking individuals seen in videos captured by body worn cameras. In this project, Dr. Qureshi's Visual Computing Lab at the University of Ontario Institute of Technology will work with Xiris to investigate pedestrian detection and segmentation methods suitable for videos exhibiting exaggerated motions. The theory and methods developed as a part of this project will not only enable us to develop automated video redaction tools, these also constitute the core functionality of privacy-preserving video surveillance systems of the future.

视频监控越来越多地被用来记录警察与公民的互动，以提高警察的安全，并遏制警察的暴行。加拿大和国际上的许多警察部门都开始了试点项目，警察将配备随身摄像头，在他们对事件做出反应时记录他们周围的情况。随身佩戴的相机引起了严重的隐私问题。例如，警察部门经常不得不发布这些视频，以回应信息自由的要求。在这些视频发布之前，法律要求警察部门隐藏或掩盖非演员的身份。这是为了保护视频中无辜个人的隐私。现有的视频密文或蒙版工具既耗时又费力。它们被设计用来处理从固定摄像机捕获的视频。穿戴在身上的相机经历了巨大的、不受控制的运动，我们需要新的工具和理论来开发自动化 用于检测和遮盖由随身相机拍摄的视频中的个人的工具。在这个项目中，安大略省理工大学库雷希博士的视觉计算实验室将与Xiris合作，研究适用于表现夸张动作的视频的行人检测和分割方法。作为该项目的一部分开发的理论和方法不仅使我们能够开发自动视频编辑工具，这些工具也构成了未来隐私保护视频监控系统的核心功能。