Methods and algorithms for multi-camera visual surveillance and monitoring

这项研究计划的目标是为智能监控系统的发展设计新的方法和算法。这项研究将侧重于建立下一代视频监测系统所需的必要组件，该系统能够提取关于正在发生的事件的高级信息，并使用户更容易获得此类系统收集的信息。重点将放在可以集成到实时监控系统中的解决方案上。寻求的解决方案将执行基于对象的视频分析，以检测、跟踪和重新识别场景中的特定对象，并识别其行为。除了开发这些单独的模块外，我们的研究计划还旨在将这四个视觉组件无缝集成到实时视频监控和监控系统中。为了验证、应用和扩展这项研究，我们的研究将在跟踪商店和餐馆顾客行为的先进监控系统的背景下进行。*目标检测方法的显著进步使其成为视频中目标提取的良好候选对象。在视频监控的环境中，即使这会导致重要的漏检率，这些检测也可以被设计为以非常低的误检率运行。处理视频使我们能够恢复在之前的帧中丢失的对象。当需要时，可以从视觉跟踪器获得物体位置中间检测。通过重新识别模块可以在多个摄像头上实现长期跟踪，该模块可以重新检测之前观察到的运动对象。最后，完全理解场景动态的最后一个基本要素是检测场景中的演员所做的动作。这四个组件代表了高级视频分析系统的基本构建块。*我们预计，拟议的研究计划将导致新的专业视频分析系统的开发。根据我们目前的合作，我们专门针对客户跟踪方面的应用，或者更广泛地说，是公共场所的事件检测和行为分析。虽然这项研究大多集中在对固定摄像头拍摄的视频的解读上，但我们也打算考虑移动摄像头的情况，如动作摄像头和可穿戴摄像头。**

The objective of this research program is to devise new methods and algorithms for the development of intelligent surveillance systems. The research will focus on the necessary components required to build the next generation of video monitoring systems capable of extracting high-level information on occurring events and give users an easier access to the information collected by such systems. The emphasis will be on solutions that can be integrated to real-time surveillance systems. The solutions sought will perform object-based video analytics for the detection, the tracking and re-identification of specific objects in the scene and the recognition of their actions. More than the development of these individual modules, our research program aims at the seamless integration of these four vision components into real-time video surveillance and monitoring systems. In order to validate, apply and expand this research, our research will be performed in the context of an advanced surveillance system for tracking the behavior of customers in store and restaurants.***The remarkable advances in object detection methods make them good candidates for object extraction in videos. In the context of video monitoring, these ones can be designed to operate at a very low false positive rate even if this leads to an important miss rate. Working with videos allow us to recover objects that were missed in previous frames. Object position in-between detections can be obtained, when required, from a visual tracker. Longer term tracking, over multiple cameras, can be achieved through a re-identification module that can re-detect a previously observed moving object. Finally, the last essential ingredient for a complete understanding of a scene's dynamic is the detection of the actions undertaken by the actors of the scene. These four components represent the fundamental building blocks of an advanced video analytics system. ***We foresee that the proposed research program will lead to the development of new specialized video analytics systems. Based on our current collaborations, we specifically target applications in customer tracking, or more generally event detection and behaviour analysis in public places. Although most of this research has a focus on the interpretation of videos captured by fixed cameras, we intent to also consider the case of moving cameras such as action cameras and wearable cameras.**