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，如物体检测、跟踪和图像识别。近年来，这些问题已经通过越来越强大的算法得到解决，在标准基准测试中产生了令人印象深刻的性能。然而，这一进展在实际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工关注和人工干预。出于这个原因，我们认为在计算机视觉的基本问题上的工作与智能视频监控应用的发展之间存在着显著的差距。目前的研究旨在通过提高视频监控系统的智能化来缩小这一差距。所谓智能，是指通过自动提取重要信息和识别异常事件来辅助人类，减少认知超载。因此，我们的长期目标是通过有效利用计算机视觉方法和视觉采集技术，开发更好地理解监控场景的解决方案。为了达到这个目标，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。这第一步对于发现优先问题和调查相关方法至关重要，以便开发强大的视频监控系统。按照这种方法，我们提出了两个短期目标。根据本提案的基本轴线，我们的目标是(1)通过优化利用深度学习和机器学习方法来提高实时对象和人的跟踪。更具体地说，我们的工作将集中在视觉目标跟踪（VOT）和人体骨骼跟踪，目标是提高跟踪精度和速度。在应用程序级别，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。我们将通过探索使用Pan-Tilt-Zoom （PTZ）相机来监控适度拥挤的场景，以及使用RGB-D相机进行基于骨骼的行为分析，分别解决室外和室内应用环境。我们的研究计划在几个方面都是创新的，因为我们研究的异常行为在计算机视觉方法中几乎没有被探索过（例如自杀企图），我们提出了在以前的作品中没有被研究过的原始方法。因此，我们期望我们的工作为智能视频监控系统产生高引用的研究和实用技术。此外，该计划是专门为培训hqp量身定制的。随着与计算机视觉和机器学习相关的公司的出现，相关的hqp将在加拿大获得高需求的专业知识。

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.