Investigating resource efficient algorithms for monitoring in video surveillance

研究视频监控中的资源高效监控算法

• 批准号: 519965-2017
• 负责人: Bilodeau, GuillaumeAlexandre
• 金额: $ 1.8万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=639580
• 关键词: Investigating; resource; efficient; algorithms; monitoring

This project aims at designing video analytic algorithms that will be implemented in a prototype smart camera.They are: 1) Detection of an unauthorized person/intrusion and 2) Detection of dwelling. Although, severalmethods exist in the scientific literature for performing such tasks, most of them are either not very robust ornot adapted for running on a embedded platform with more limited resources (processor and memory). Ourpartner company wishes to perform on an embedded system these tasks in real-time. Our investigations willtherefore focus on researching methods and algorithms that can be implemented efficiently in a smart camera,yet that are robust, for detecting intrusion and dwelling in surveillance videos.We will investigate how subsampling can be used to process fewer pixels per frame, but yet detect the objectsof interest in background subtraction algorithms. We will also investigate the background update mechanismsto select or adapt only the most useful ones. Furthermore, we will design a motion history approach toaccumulate the detected foreground detections. Decisions will be made based on the accumulation map usingmachine learning techniques.

该项目旨在设计将在原型智能摄像机中实现的视频分析算法。它们是：1）未经授权的人/入侵的检测和2）住宅的检测。尽管在科学文献中存在用于执行此类任务的几种方法，但它们中的大多数要么不是非常鲁棒，要么不适合在具有更有限资源（处理器和内存）的嵌入式平台上运行。我们的合作伙伴公司希望在嵌入式系统上实时执行这些任务。因此，我们的研究将集中在研究方法和算法，可以有效地实现在智能摄像机，但这是强大的，检测入侵和居住在监控视频。我们将研究如何子采样可以用来处理每帧更少的像素，但仍然检测目标的背景减法算法。我们还将研究背景更新机制，以选择或调整最有用的机制。此外，我们将设计一个运动历史的方法来累积检测到的前景检测。决策将基于使用机器学习技术的累积图做出。