From Frames to Events: A Statistical Approach to Activity Analysis in Multi-Camera Systems

从框架到事件：多摄像机系统中活动分析的统计方法Venkatesh Saligrama和Janusz Konrad，波士顿大学，MA 02215与其他传感器不同，摄像机提供出色的分辨率，长视角范围，宽视野和低延迟，从而允许无处不在的广域视觉监视。然而，今天部署的大多数网络摄像机都是简单的捕获/压缩/传输设备，最多支持基本的运动检测;所有更高级别的处理都是高度集中的。这种集中式架构源于以人为中心的视觉分析以及有限的相机处理能力，并且无法扩展到大型多相机系统。美国目前使用的监控摄像头超过3000万个，每周产生40亿小时的视频画面，人类操作员的监控显然是不可持续的。因此，需要一个自主的、分布式的、高带宽效率的、实时的视频分析系统，本项目为建立这样一个系统迈出了一步。其核心是一种新颖的活动发现和分析统计框架，该框架与集中式模型不同，并利用摄像机节点的处理能力。对象被识别、跟踪和测试异常，本项目正在开发的方法采用像素级的活动分析。如果异常活动被可靠地识别，则对象提取和跟踪集中在感兴趣的区域，因此相对简单，因为没有杂波。为了可靠地识别像素级异常，或者更一般的活动，使用了一种新型的基于事件的视频表示，该表示将视频分解为iid样本，从而有助于统计学习的应用。为了促进多摄像机协作，利用动态事件的几何不变性，从而绕过了与依赖于视角的3-D几何相关的困难问题。

From Frames to Events: A Statistical Approach to Activity Analysis in Multi-Camera SystemsVenkatesh Saligrama and Janusz Konrad, Boston University, MA 02215Unlike other sensors, cameras provide excellent resolution, long viewing range,wide field of view and low latency thus permitting pervasive, wide-area visualsurveillance. However, most network cameras deployed today are simplecapture/compression/transmission devices, at most supporting rudimentary motiondetection; all higher-level processing is highly centralized. This centralizedarchitecture stems from human-centric visual analytics as well as limitedin-camera processing capacity, and is not scalable to large multi-camerasystems. With over 30 million surveillance cameras in use in the United Statestoday, that produce 4 billion hours of video footage per week, monitoring byhuman operators is obviously not sustainable. An autonomous, distributed,bandwidth-efficient, real-time video analytics system is needed.This project makes a step towards building such a system. At its core is anovel statistical framework for activity discovery and analysis that departsfrom the centralized model and leverages processing power of camera nodes.While traditional activity analysis operates at object level, e.g., objects areidentified, tracked, and tested for abnormality, methods under development inthis project employ activity analysis at pixel level. If the abnormal activityis reliably identified, then object extraction and tracking focus on region ofinterest and thus are relatively straightforward, on account of absence ofclutter. In order to reliably identify pixel-level abnormalities, or moregenerally activities, a novel event-based video representation is used thatdecomposes video into iid samples lending itself to the application ofstatistical learning. In order to facilitate multi-camera collaboration,geometric invariance of dynamic events is exploited thus bypassing thedifficult issues related to 3-D geometry dependent on viewing angles.