Inferring 3D information from a monocular camera

从单目相机推断 3D 信息

• 批准号: 524235-2018
• 负责人: Lalonde, JeanFrançois
• 金额: $ 1.56万
• 依托单位: Université Laval
• 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=678553
• 关键词: Inferring; 3D; information; monocular; camera

Flying drones are becoming ubiquitous. From automated mail delivery to military applications, these flying robots are bound to be present in our skies in the near future. While they can provide interesting benefits, their presence also pose potential risks ranging from the disruption of air travel to national security threats. It is therefore critical that we develop systems to detect and track drones as they are flying at potentially high speed. In this grant application, we propose to develop computer vision algorithms for tracking high speed flying objects. The algorithms must be robust to occlusions, operate with real-world sensors, and adapt to objects at varying distances from the camera. To do so, our key observation is that the apparent speed and size of the object in the image plane depend on its distance to the camera: the farther away the object, the smaller and the slower it will appear in the image (and vice-versa). This allows us to elaborate a research methodology based on three sub-objectives. The first sub-objective is to track objects are very high speed that fly close to the camera. For this, we will rely on an event-based camera: a novel type of camera that reports changes as they occur and are thus extremely fast. The second sub-objective is to track far away objects at lower speed but with lower resolution. In this case, we will rely on high resolution cameras. Finally, the last sub-objective will combine the first two in a coherent framework. The proposed project will benefit the Canadian industry in several ways. In particular, the industrial partner Thales Canada identifies at least two potential applications. First, drone detection is critical for air traffic management. Early detection of flying objects is crucial to prevent collisions with aircrafts. Second, evidence exists that terrorist groups are showing a growing interest in militarizing drones to deliver explosive payloads in densely populated urban areas. This research will enable early detection of such threats in urban context, thus enhancing the Canadian security on its territory and protect its critical infrastructures.

无人驾驶飞机正变得无处不在。从自动邮件递送到军事应用，这些飞行机器人在不久的将来一定会出现在我们的天空中。虽然它们可以提供令人感兴趣的好处，但它们的存在也构成潜在的风险，从扰乱航空旅行到国家安全威胁。因此，我们开发系统来检测和跟踪无人机是至关重要的，因为它们正在以潜在的高速飞行。在这项拨款申请中，我们建议开发用于跟踪高速飞行物体的计算机视觉算法。这些算法必须对遮挡具有鲁棒性，与真实世界的传感器一起操作，并适应距离相机不同距离的对象。要做到这一点，我们的关键观察是，物体在图像平面中的表观速度和大小取决于它到相机的距离：物体越远，它在图像中出现的速度就越小和越慢（反之亦然）。这使我们能够根据三个子目标详细阐述研究方法。第一个子目标是跟踪非常接近相机的高速飞行物体。为此，我们将依赖于基于事件的相机：一种新型的相机，可以在发生变化时报告变化，因此速度非常快。第二子目标是以较低的速度但以较低的分辨率跟踪远处的物体。在这种情况下，我们将依靠高分辨率相机。最后，最后一个次级目标将把前两个目标联合收割机结合在一个连贯的框架内。拟议的项目将在几个方面使加拿大工业受益。特别是，工业合作伙伴泰雷兹加拿大公司确定了至少两个潜在的应用。首先，无人机检测对于空中交通管理至关重要。早期发现飞行物体对于防止与飞机相撞至关重要。第二，有证据表明，恐怖主义团体越来越有兴趣将无人驾驶飞机军事化，以便在人口稠密的城市地区运送爆炸物。这项研究将有助于及早发现城市环境中的这种威胁，从而加强加拿大领土的安全，保护其重要基础设施。