Inferring 3D information from a monocular camera

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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.