Multi-Object Video Behaviour Modelling for Abnormality Detection and Differentiation

英国有超过420万个闭路电视(CCTV)监控摄像头在运行，全世界还有更多的闭路电视(CCTV)监控摄像头，收集大量视频数据用于安全、安全以及基础设施和设施管理目的。一个典型的现有闭路电视系统依赖于一个中央控制室的少数几名操作员来监控数百个摄像头的视频输入。太多的摄像机和太少的操作员使系统装备不足，无法完成检测需要立即和适当反应的事件和异常情况的任务。因此，现有闭路电视监控系统的使用主要限于尸检分析。因此，对自动化智能系统的需求越来越大，这些系统用于分析大量监控录像的内容并及时和有力地触发警报。这类系统最关键的组件和功能之一是监控视频中捕获的对象行为，并检测/预测任何可能对公共安全和安保构成威胁的可疑和异常行为。该项目旨在为一种创新的智能视频分析系统开发基础能力，以检测公共场所的异常视频行为。更具体地说，该项目将解决三个悬而未决的问题：1.开发用于异常行为检测的时空视觉上下文的新模型。行为本质上是上下文感知的，通过场景布局和给定场景中活动的时间性质施加的限制来表现出来。因此，同样的行为可以被认为是正常的或不正常的，这取决于它发生的地点和时间。我们的目标是超越最先进的语义场景建模方法，通过开发更全面的动态视觉上下文的时空模型来实现对场景布局(如入口点和出口点)的建模。2.开发一种新的多对象行为模型，用于实时检测和区分涉及多个对象相互作用的复杂视频行为中的异常(例如，一群人在火车站售票处前相遇，然后前往不同的站台)。3.开发一种新的在线自适应学习算法，用于估计待开发的行为模型的参数。虽然许多现有的CCTV控制系统中已经提供了视频异常检测工具，但操作员往往不愿使用它们，因为在给定不断变化的视觉环境的情况下，有太多的参数需要针对不同的场景进行调整和重新调整。通过增量和自适应学习算法，我们的行为模型可以在很长一段时间内用于不同的监控场景，而只需最少的人工干预。更重要的是，使用该算法，我们的行为模型将对视觉上下文的变化(因此是正常/异常的定义)以及人类操作员对模型的异常检测输出的有价值的反馈都变得自适应。

There are over 4.2 million closed-circuit television (CCTV) surveillance cameras operational in the UK and many more worldwide, collecting a colossal amount of video data for security, safety, and infrastructure and facility management purposes. A typical existing CCTV system relies on a handful of human operators at a centralised control room for monitoring video inputs from hundreds of cameras. Too many cameras and too few operators leave the system ill equipped to fulfil the task of detecting events and anomalies that require immediate and appropriate response. Consequently, the use of the existing CCTV surveillance systems is limited predominately to post-mortem analysis. There is thus an increasing demand for automated intelligent systems for analysing the content of the vast quantities of surveillance videos and triggering alarms in a timely and robust fashion. One of the most critical components and functionalities of such a system is to monitor object behaviour captured in the videos and detect/predict any suspicious and abnormal behaviour that could pose a threat to public safety and security. This project aims to develop underpinning capabilities for an innovative intelligent video analytics system for detecting abnormal video behaviour in public spaces. More specifically, the project will address three open problems:1.To develop a new model for spatio-temporal visual context for abnormal behaviour detection. Behaviours are inherently context-aware, exhibited through constraints imposed by scene layout and the temporal nature of activities in a given scene. Consequently, the same behaviour can be deemed as either normal or abnormal depending on where and when it occurs. We aim to go beyond the state-of-the-art semantic scene modelling approaches, most of which are focused solely on modelling scene layout such as entry and exit points, by developing a more comprehensive spatio-temporal model of dynamic visual context. 2.To develop a novel multi-object behaviour model for real-time detection and differentiation of abnormalities in complex video behaviours that involve multiple objects interacting with each other (e.g. a group of people meet in front of a ticket office at a train station and then go to different platforms). 3.To develop a novel online adaptive learning algorithm for estimating the parameters of the behaviour model to be developed. Although video abnormality detection tools are already available in many existing CCTV control systems, human operators are often reluctant to use them because there are too many parameters to tune and re-tune for different scenarios given changing visual context. With the incremental and adaptive learning algorithm our behaviour model can be used for different surveillance scenarios over a long period of time with minimal human intervention. More importantly, using the algorithm, our behaviour model will become adaptive to both changes of visual context (therefore the definition of normality/abnormality), and valuable feedbacks from human operators on the abnormality detection output of the model.