Intelligent surveillance for event detection

事件检测的智能监控

• 批准号: 478782-2015
• 负责人: Noumeir, Rita
• 金额: $ 1.82万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=579775
• 关键词: Intelligent; surveillance; event; detection

Detecting and informing users about events of interest is the key function in intelligent video surveillance systems. Among the most important requirements is that the real-time surveillance system must: 1) operate continuously and autonomously in real-world conditions, and 2) be reliable without overloading the user with useless detection.These are the two specific problems that we are addressing in the proposed project, where we aim to develop an efficient video surveillance system that intelligently detects and reports events of interest to the user. This is a very challenging problem since the visual characteristics of an unconstrained scene are unstable, mainly due to large variations in illumination. On the other hand, the definition of an event of interest may vary between users. Currently, most of the existing systems use visible-light cameras and operate in constantly and evenly illuminated areas to detect simple events (e.g. intrusion), using basic change detection techniques. Some of the recent systems give the users the possibility to customize the detection by drawing interest zones and setting motion parameters such as the direction and velocity. But, this customization remains a complex and time-consuming process that most users are not willing to follow. Our investigation will therefore focus on designing methods and algorithms for detecting events of interest in the user`s context. The novelty of our approach is twofold. The first consists in using depth maps that can be obtained regardless of the illumination conditions, using recent cost effective RGB-D sensors. As a consequence, more robust appearance models can be constructed by adding 3D position information to standard color features. The second consists in enabling the user to rate the importance of a reported event. Therefore, detection would be improved by tuning the alerts for a given user based on his/ her past feedback. This project objectives include the development of: 1) models for actions and events in the 4D domain (3D space and time), 2) methods for recognizing actions and events, and 3) methods to learn from user feedback for future detection.

检测和通知用户感兴趣的事件是智能视频监控系统的关键功能。其中最重要的要求是，实时监控系统必须：1)在现实条件下连续自主运行，2)可靠，不会因无用的检测而使用户过载。这是我们在提议的项目中要解决的两个具体问题，我们的目标是开发一种高效的视频监控系统，可以智能地检测和报告用户感兴趣的事件。这是一个非常具有挑战性的问题，因为无约束场景的视觉特征是不稳定的，主要是由于光照的巨大变化。另一方面，不同用户对感兴趣的事件的定义可能不同。目前，大多数现有系统使用可见光摄像机，并在持续和均匀照明的区域运行，使用基本的变化检测技术来检测简单事件（例如入侵）。最近的一些系统使用户可以通过绘制兴趣区域和设置运动参数（如方向和速度）来定制检测。但是，这种定制仍然是一个复杂且耗时的过程，大多数用户都不愿意遵循。因此，我们的研究将集中在设计方法和算法来检测用户上下文中感兴趣的事件。我们方法的新颖之处有两个。第一种方法是使用最新的具有成本效益的RGB-D传感器，无论光照条件如何，都可以获得深度图。因此，通过将3D位置信息添加到标准颜色特征中，可以构建更健壮的外观模型。第二是让用户能够对所报告事件的重要性进行评级。因此，通过根据给定用户过去的反馈调整警报，可以改进检测。该项目的目标包括：1)4D域（3D空间和时间）的动作和事件模型，2)识别动作和事件的方法，以及3)从用户反馈中学习用于未来检测的方法。