Intelligent video surveillance for abnormal event detection

开发能够自动检测异常事件的智能视频监控系统需要解决具有挑战性的计算机视觉问题，例如目标检测，跟踪和图像识别。近年来，这些问题已经得到解决，越来越多的更强大的算法，标准基准测试产生令人印象深刻的性能。然而，这一进展在现实世界的应用中并不令人印象深刻，绝大多数视频监控系统仍然需要人类的关注和手动干预。出于这个原因，我们认为，计算机视觉的基本问题的工作之间存在着显着的差距，智能视频监控应用的发展。 本研究旨在通过提高视频监控系统的智能性来缩小这一差距。所谓智能，我们的意思是通过自动提取重要信息和识别异常事件来帮助人类并减少认知过载。因此，我们的长期目标是开发解决方案，通过有效利用计算机视觉方法和视觉采集技术，更好地了解监控场景。为了实现这一目标，我们采取了一种方法，从研究最先进的应用程序的局限性开始。这第一步是至关重要的检测优先问题和调查相关的方法，以开发强大的视频监控系统。在此基础上，我们提出了两个短期目标。根据该提案的基本轴，我们的目标是（1）通过优化深度学习和机器学习方法来改善实时对象和人员跟踪。更具体地说，我们的工作将集中在视觉对象跟踪（VOT）和人体骨骼跟踪，目标是提高跟踪精度和速度。在应用层面上，我们将利用我们在跟踪问题上的工作来（2）检测和分析异常事件。我们将分别解决室外和室内应用环境，探索使用泛变焦（PTZ）摄像头来监控适度拥挤的场景，以及使用RGB-D摄像头进行基于摄像头的行为分析。 我们的研究计划在几个方面都是创新的，因为我们研究了计算机视觉方法中几乎未被探索的异常行为（例如自杀企图），并且我们提出了在以前的工作中没有被研究过的原创方法。因此，我们希望我们的工作产生高度引用的研究和智能视频监控系统的实用技术。此外，该计划是专门为培训HQP量身定制的。随着计算机视觉和机器学习相关公司的出现，相关的HQP将获得加拿大各地高需求的专业知识。

Developing intelligent video surveillance systems that are able to automatically detect abnormal events requires solving challenging computer vision problems, such as object detection, tracking, and image recognition. During recent years, such problems have been addressed by increasingly more robust algorithms, producing impressive performances on standard benchmarks. However, this progress is not as impressive in real-world applications, where the vast majority of video surveillance systems still require human attention and manual intervention. For this reason, we argue that a significant gap exists between the work on fundamental problems of computer vision, and the development of intelligent video surveillance applications. The present research aims at narrowing this gap, by enhancing the intelligence of video surveillance systems. By intelligence, we mean to assist humans and reduce cognitive overload by extracting important information and identifying abnormal events automatically. Our long-term goal is thus to develop solutions to better understand monitored scenes, through the efficient exploitation of computer vision methods and visual acquisition technology. To reach this goal, we take an approach that starts by studying the limitations of state-of-the-art applications. This first step is crucial to detect priority problems and investigate relevant methodologies in order to develop robust video surveillance systems. Following this approach, we propose 2 short-term objectives. According to the fundamental axis of this proposal, we aim at (1) improving real-time object and person tracking, by making the optimal exploitation of deep learning and machine learning methods. More specifically, our work will be focused on visual object tracking (VOT) and human skeleton tracking, with the objective of increasing both tracking accuracy and speed. At the application level, we will take advantage of our work on the tracking problem for (2) detecting and analyzing abnormal events. We will address both outdoor and indoor application contexts, respectively, by exploring the use of a Pan-Tilt-Zoom (PTZ) camera to monitor moderately crowded scenes, and an RGB-D camera for skeleton-based behavior analysis. Our research program is innovative in several respects, as we study abnormal behaviors that are almost unexplored in a computer vision approach (e.g. suicide attempts), and we propose original methodologies that have not been investigated in previous works. We thus expect our work to produce highly cited research and practical techniques for intelligent video surveillance systems. Moreover, the program is specifically tailored for training HQPs. With the emergence of companies related to computer vision and machine learning, involved HQPs will acquire an expertise in high demand across Canada.