Video analytics for abnormal event detection

用于异常事件检测的视频分析

• 批准号: 528786-2018
• 负责人: Bilodeau, GuillaumeAlexandre
• 金额: $ 3.06万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688569
• 关键词: Video; analytics; abnormal; event; detection

The goal of this project is to design new methods to detect abnormal events in videos and to generate semantic labels to describe objects in videos. For abnormal event detection, we will focus on the detection of the loitering behavior, which consists in spending a large amount of time in a given area. Since the loiterer can exit and re-enter the field of view of the camera, we will also need to investigate person re-identification. The loitering detection is meant to be run on each camera to lighten the image processing load at the main visual surveillance hub. Therefore, the scientific challenges will be to differentiate individuals that may have similar appearance, and track the individuals, while at the same time designing methods that can be implemented on an embedded system. To solve this problem, we will rely on machine learning techniques to learn to compare the appearance of objects. This will be useful for both tracking and person re-identification. We will also study the problem of generating semantic labels to describe the individuals that are tracked. This is to accelerate search in videos. We wish to generate labels that describe the general appearance of the person, like the color of their clothes, their type (skirt, pant, etc.), if they are wearing a hat or carrying a bag. To solve this problem we will learn to generate labels from the appearance of the individuals in the image. The learned appearance to semantic labels neural network will also be useful for person re-identification.

该项目的目标是设计新的方法来检测视频中的异常事件，并生成语义标签来描述视频中的对象。对于异常事件的检测，我们将重点关注徘徊行为的检测，徘徊行为包括在给定区域花费大量时间。由于游荡者可以退出并重新进入摄像机的视野，因此我们还需要调查人员的重新识别。游荡检测将在每个摄像机上运行，以减轻主要视觉监控中心的图像处理负荷。因此，科学上的挑战将是区分可能具有相似外观的个体，并跟踪个体，同时设计可以在嵌入式系统上实现的方法。为了解决这个问题，我们将依靠机器学习技术来学习比较物体的外观。这对跟踪和人员重新识别都很有用。我们还将研究生成语义标签来描述被跟踪的个体的问题。这是为了加快视频搜索。我们希望生成描述人的一般外观的标签，比如他们衣服的颜色，他们的类型（裙子，裤子等），如果他们戴着帽子或拿着包。为了解决这个问题，我们将学习从图像中个体的外观生成标签。对语义标签神经网络的学习也将有助于人的再识别。