Workshop on Frontiers in Image and Video Analysis

图像和视频分析前沿研讨会

• 批准号: 1402723
• 负责人: Rama Chellappa
• 金额: $ 5.59万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-15 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1402723&HistoricalAwards=false
• 关键词: Workshop; Frontiers; Image; Video; Analysis

The bombing attacks at the Boston Marathon in April 2013 presented the law enforcement community with significant challenges in terms of the volume and variety of video and still images acquired in the course of the investigation. Tens of thousands of individual media files in multiple formats were submitted from a variety of sources. These sources included broadcast television feeds, private Close-Circuit Television (CCTV) systems, mobile device photographs and videos recovered from the scene, as well as photographs and videos submitted by the public. Teams of analysts reviewed this evidence using mostly manual processes to determine the sequence of events before and after the bombing, ultimately leading to a quick resolution of the case. In the aftermath, it has become evident that the proliferation of video and image recording devices in fixed and mobile devices make it inevitable that a similar situation will occur in future events. As a result, it is incumbent upon the law enforcement community and the U.S. Government at large to further explore the use of automated approaches, available today or in the coming years, to better organize and analyze such large volumes of multimedia data. The findings of this workshop will help define the future research agenda. The problem of searching for actionable intelligence information from unconstrained images and videos is an unsolved problem. Solving this involves addressing many sub-problems such as video summarization, shot detection/scene change detection, geo-tagging, robust face recognition, human action recognition, semantic description, image recognition and designing human in the loop systems. In addition, issues such as data collection and performance evaluation have to be addressed. Given that several hundreds of videos and a large collection of still images may be available for analysis, there is a great need to develop robust computer vision techniques. While many existing computer vision algorithms perform reasonable well in constrained acquisition conditions, their performance when unconstrained images and videos are given, is less than satisfactory. This workshop precisely addresses the challenges that arise in analyzing a large collection of unstructured image/video collection. This workshop explores the state of the art in algorithms being developed in academia that can support forensic analysis and identification in large volumes of images and videos (e.g., multimedia). The workshop informs long- and near-term research and development efforts aimed at optimally addressing this situation in the future. The workshop identifies those video and image analysis problems which are: (1) Considered solved (i.e., ready to deploy in specific operational scenarios); (2) Nearly solved (i.e., could lead to solutions with one to three years of development); and (3) Over-the-Horizon problems (i.e., those challenges requiring concerted effort over the next 3-5 years and beyond).

2013年4月波士顿马拉松爆炸袭击事件在调查过程中获得的视频和静止图像的数量和种类方面给执法界带来了重大挑战。从各种来源提交了数以万计的多种格式的单个媒体文件。这些来源包括广播电视、私人闭路电视系统、从现场回收的移动终端照片和视频，以及公众提交的照片和视频。分析小组审查了这些证据，主要使用人工程序，以确定爆炸前后的事件顺序，最终导致案件的迅速解决。在此之后，已经变得明显的是，固定和移动的设备中的视频和图像记录设备的扩散使得在未来事件中将不可避免地发生类似的情况。因此，执法界和美国政府有责任进一步探索使用自动化方法，今天或未来几年，以更好地组织和分析如此大量的多媒体数据。这次研讨会的结果将有助于确定未来的研究议程。从不受约束的图像和视频中搜索可操作的情报信息是一个尚未解决的问题。解决这个问题涉及到解决许多子问题，如视频摘要，镜头检测/场景变化检测，地理标记，鲁棒的人脸识别，人类动作识别，语义描述，图像识别和设计人在回路系统。此外，还必须解决数据收集和业绩评价等问题。鉴于数百个视频和大量静止图像可用于分析，因此非常需要开发鲁棒的计算机视觉技术。虽然许多现有的计算机视觉算法在有约束的采集条件下表现得相当好，但当给出无约束的图像和视频时，它们的性能不太令人满意。这个研讨会正好解决了在分析大量非结构化图像/视频集合时出现的挑战。 该研讨会探讨了学术界正在开发的算法的最新技术，这些算法可以支持大量图像和视频的法医分析和识别（例如，多媒体）。研讨会通报了长期和近期的研究和开发工作，旨在最佳地解决这一问题的未来。研讨会确定了以下视频和图像分析问题：（1）认为已解决（即，已准备好在特定操作场景中部署）;（2）几乎已解决（即，可以通过一到三年的发展找到解决方案）;以及（3）超视距问题（即，这些挑战需要在今后3-5年及以后作出协调一致的努力）。