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，全球范围内有更多，收集了大量的视频数据，用于安全，基础设施和设施管理。典型的现有CCTV系统依赖于集中控制室的少数操作人员来监控来自数百个摄像头的视频输入。摄像机太多而操作员太少，使系统无法完成检测需要立即作出适当反应的事件和异常情况的任务。因此，现有闭路电视监视系统的使用主要限于事后分析。因此，对用于分析大量监控视频的内容并以及时和鲁棒的方式触发警报的自动化智能系统的需求日益增加。这种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/预测可能对公共安全和安保构成威胁的任何可疑和异常行为。该项目旨在为创新的智能视频分析系统开发基础功能，以检测公共场所的异常视频行为。更具体地说，该项目将解决三个开放的问题：1。开发一个新的时空视觉背景异常行为检测模型。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所施加的约束来表现。因此，同样的行为可以被认为是正常的或不正常的，这取决于它发生在哪里和什么时候。我们的目标是超越国家的最先进的语义场景建模方法，其中大部分是只专注于建模场景布局，如入口和出口点，通过开发一个更全面的时空模型的动态视觉环境。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，然后前往不同的平台）。3.提出了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。虽然视频异常检测工具已经在许多现有的CCTV控制系统中可用，但是人类操作员通常不愿意使用它们，因为在给定变化的视觉环境的情况下，有太多的参数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，只需最少的人为干预。更重要的是，使用该算法，我们的行为模型将适应视觉环境的变化（因此是正常/异常的定义），以及人类操作员对模型异常检测输出的有价值的反馈。

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.