Smart object detection and tracking for video surveillance

我和我的合作者所从事的研究是在计算机视觉的一般领域，试图赋予机器视觉。从理论的角度来看，这是一项重要的努力，因为它涉及复杂信息处理的基本问题，并可能产生影响我们对自然视觉系统（例如人类）理解的结果。从实用的角度来看，能看见东西的机器有潜力以一种比目前更自然、更有用的方式与人类互动。本课题研究解决了当前视频监控中的一个突出问题。它旨在自动检测和跟踪监控视频中的“有趣”物体。感兴趣的对象将被定义为那些有助于提醒安全人员需要进行干预的对象，例如，在他们通常会走路的地方奔跑的人或在错误方向行驶的车辆。现有的视频目标检测和跟踪技术的一个关键限制是它无法涵盖现实世界监控中存在的广泛可变性。这一挑战将通过利用我们实验室最近在动态视频模式建模方面的工作来解决，这些工作先前已被证明对各种任务很有用，包括目标跟踪和人体运动分析。我们在这些相关领域取得成功的关键是我们能够以简洁和准确的方式在视频中模拟复杂的现实世界动态。我们的工作以三种方式为加拿大做出贡献。1)它为视频监控的更广泛应用铺平了道路，因为可以包含更多的现实场景。这一进步将进一步推动加拿大工业在日益增长的视频监控领域取得成功。2)更普遍

The research that my collaborators and I pursue is in the general area of computer vision, the attempt to endow machines with a sense of sight. From a theoretical perspective, this is an important endeavor as it bears on basic issues in complex information processing and may yield results that bear on our understanding of natural vision systems (e.g., humans). From a practical perspective, machines that can see have the potential to interact with humans in a more natural and useful fashion than is currently the case. The research addressed in the current project attacks an outstanding problem in video surveillance. It is aimed at automated detection and tracking of "interesting" objects in surveillance video. Objects of interest will be defined as those useful for alerting security personnel of the need for intervention, e.g., humans running where they normally would walk or vehicles moving in the wrong direction. A key limitation of extant technology for object detection and tracking in video is its inability to encompass the wide range of variability that is present in real-world surveillance. This challenge will be attacked by leveraging recent work in our lab in modeling dynamic video patterns that previously has proven useful for a variety of tasks, including target tracking and human motion analysis. The key to our success in these related areas is our ability to model complicated real-world dynamics in video in a concise and accurate fashion. Our work contributes to Canada in 3 ways. 1) It paves the way for wider applications of video surveillance as more real-world scenarios can be encompassed. This advance will further the success of Canadian industry in the growing area of video surveillance. 2) It more generally