Human motion tracking-by-detection using point cloud data from multiple depth sensors

使用来自多个深度传感器的点云数据进行人体运动检测跟踪

• 批准号: RGPIN-2016-04165
• 负责人: Czarnuch, Stephen
• 金额: $ 2.19万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=708768
• 关键词: Human; motion; tracking; detection; using

In research and practice, using computers to track people is important for many reasons including security, healthcare delivery, computer interfaces, and video gaming. Colour video cameras, and more recently colour cameras that can detect how far objects are from the cameras, have been used to track people in indoor environments without the use of any special equipment (like wearable sensors or sensors placed around the room). However, these only work well in ideal indoor environments rooms with objects that don't move very often, and rooms without a lot of clutter. In cases where rooms are dynamic (e.g., in hospital settings) or rooms are cluttered (e.g., busy homes), these methods of tracking do not work well. In these more challenging indoor environments, these systems often do not work because the single camera cannot see enough of the room to track the people or because the people are blocked by the clutter in the room. This work wants to find a way to reliably track people in more challenging indoor environments. Adding more cameras is a possible solution, but programming a computer to track people with more than one camera is difficult. Currently, images from more than one camera can only reliably be put together if the images are almost the same (e.g., looking at the room from almost the same angle). Even if the cameras are looking at the same person or part of the room from different angles, it is very hard to put together the images. The proposed work will partially align images from more than one camera using the natural motion of people as they walk around a room. Once partially aligned, existing ways of reliably putting together images can then be used. After the images from the cameras are put together into one complete image, the actions of the people will be tracked to identify what they are doing. However, so far tracking what people are doing using an image made from more than one camera has been hard. This work will also develop a new way of tracking what people are doing from this combined image in challenging indoor environments. Tracking people in more challenging indoor environments will allow helpful technologies to be used in settings like hospitals, long-term care homes, and even in the homes of healthy people. In this way, technology can help us in many ways like improving the way we get help in hospitals, making us safer, making it easier to use computers, and making video games more fun and realistic to play. This type of research is also very timely and important to other researchers, at the state-of-the-art of the field, because it will allow others to easily combine images from multiple cameras and track what people are doing in most indoor environments. This research will result in the creation of a prototype system that will automatically start tracking people in an indoor environment after users place cameras anywhere they want around a room.

在研究和实践中，使用计算机跟踪人是很重要的，原因有很多，包括安全，医疗保健提供，计算机接口和视频游戏。彩色摄像机，以及最近可以检测物体距离摄像机多远的彩色摄像机，已被用于在室内环境中跟踪人，而无需使用任何特殊设备（如可穿戴传感器或放置在房间周围的传感器）。然而，这些只有在理想的室内环境中才能很好地工作，房间里的物体不经常移动，房间里没有很多杂物。在房间是动态的情况下（例如，在医院环境中）或房间杂乱（例如，忙碌的家庭），这些方法的跟踪不工作很好。在这些更具挑战性的室内环境中，这些系统通常无法工作，因为单个摄像头无法看到足够的房间来跟踪人，或者因为人被房间中的杂乱物阻挡。 这项工作希望找到一种方法，在更具挑战性的室内环境中可靠地跟踪人们。增加更多的摄像头是一个可能的解决方案，但编程一台计算机来跟踪一个以上的摄像头的人是困难的。目前，来自多于一个相机的图像只有在图像几乎相同（例如，从几乎相同的角度看房间）。即使摄像机从不同的角度看同一个人或房间的一部分，也很难将图像放在一起。拟议的工作将部分对齐图像从一个以上的相机使用的自然运动的人，因为他们走在一个房间。一旦部分对齐，就可以使用现有的可靠地将图像放在一起的方法。当摄像机拍摄的图像被整合成一张完整的图像后，人们的行为将被跟踪，以识别他们在做什么。 然而，到目前为止，使用多个摄像头拍摄的图像来跟踪人们正在做什么是很困难的。 这项工作还将开发一种新的方法，跟踪人们在具有挑战性的室内环境中从这种组合图像中所做的事情。 在更具挑战性的室内环境中跟踪人们将使有用的技术能够在医院，长期护理院甚至健康人的家中使用。通过这种方式，技术可以在很多方面帮助我们，比如改善我们在医院获得帮助的方式，让我们更安全，让电脑更容易使用，让视频游戏更有趣，更逼真。这种类型的研究对其他研究人员来说也是非常及时和重要的，在该领域的最先进水平，因为它将允许其他人轻松地联合收割机图像从多个摄像机和跟踪人们在大多数室内环境中做什么。这项研究将导致创建一个原型系统，该系统将在用户将摄像头放置在房间周围的任何地方后自动开始跟踪室内环境中的人。