Methods and algorithms for multi-camera visual surveillance and monitoring

本研究计划的目的是为智能监控系统的发展设计新的方法和算法。该研究将集中于建立下一代视频监控系统所需的必要组成部分，这些系统能够提取有关发生事件的高级信息，并使用户更容易获得这些系统收集的信息。重点将放在可以集成到实时监视系统的解决方案上。所寻求的解决方案将执行基于对象的视频分析，用于检测、跟踪和重新识别场景中的特定对象以及识别其行为。除了这些单独模块的开发，我们的研究计划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。在视频监控的背景下，这些可以被设计成一个非常低的误报率，即使这会导致一个重要的漏检率。使用视频使我们能够恢复在前一帧中丢失的对象。当需要时，可以从视觉跟踪器获得中间检测中的目标位置。通过一个重新识别模块，可以在多个摄像机上实现更长期的跟踪，该模块可以重新检测先前观察到的移动物体。最后，完全理解场景动态的最后一个基本要素是对场景演员所采取的行动的检测。这四个组件代表了高级视频分析系统的基本构建模块。我们预见，拟议的研究计划将导致新的专业视频分析系统的发展。基于我们目前的合作，我们专门针对客户跟踪的应用程序，或者更普遍的公共场所事件检测和行为分析。虽然大部分研究都集中在对固定摄像机拍摄的视频的解释上，但我们也打算考虑移动摄像机的情况，如运动摄像机和可穿戴摄像机

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.**