Workshop on Frontiers in Image and Video Analysis

2013年4月波士顿马拉松赛的爆炸袭击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部门带来了重大挑战。来自不同来源的数以万计的多种格式的单独媒体文件被提交。这些来源包括广播电视信号、私人闭路电视(CCTV)系统、从现场找到的移动设备照片和视频，以及公众提交的照片和视频。分析团队审查了这些证据，主要是使用人工过程来确定爆炸前后的事件顺序，最终导致案件迅速得到解决。事后，显而易见的是，视频和图像记录设备在固定和移动设备中的激增，使得类似的情况不可避免地会在未来的活动中发生。因此，执法部门和整个美国政府有责任进一步探索使用今天或未来几年可用的自动化方法，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。解决这一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、稳健的人脸识别、人类行为识别、语义描述、图像识别和设计人在环系统。此外，还必须解决数据收集和业绩评估等问题。鉴于可能有数百个视频和大量静止图像可供分析，因此非常需要开发稳健的计算机视觉技术。虽然许多现有的计算机视觉算法在受限的捕获条件下表现得相当好，但当给定非受限的图像和视频时，它们的性能却不太令人满意。该研讨会准确地解决了在分析大量非结构化图像/视频集合时出现的挑战。本研讨会探讨了学术界正在开发的能够支持大量图像和视频(例如，多媒体)中的法医分析和识别的算法的最新水平。研讨会为长期和近期的研究和开发工作提供了信息，旨在以最佳方式解决未来的这一情况。讲习班确定了那些视频和图像分析问题，它们是：(1)被认为已解决(即，可在具体业务方案中部署)；(2)几乎已解决(即，可导致一至三年开发的解决方案)；(3)超地平线问题(即，那些需要在未来3-5年及以后共同努力的挑战)。

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).