Pixels in an event camera operate asynchronously and independently, reporting changes in intensity as events - tuples of (x,y) position, polarity s and timestamp t at microsecond resolution. Event cameras operate at low power (≈ 5mW) and respond to changes in the scene with a latency on the order of microseconds. These properties make event cameras an exciting candidate for eye tracking sensors on mobile platforms such as Augmented/Virtual Reality (AR/VR) headsets, since these systems have hard real-time and power constraints. One proven method for eye tracking and gaze estimation is corneal glint detection. We exploit the fact that corneal glint tracking only requires a sparse set of pixels in the image, by making use of the natural sparsity of event cameras, which only detect changes in the scene. To enhance this effect, we design an illumination scheme, Coded Differential Lighting, which enhances specular reflections, suppresses all other events, and solves the light-to-glint correspondence. This is the first purely event-based corneal glint detection and tracking algorithm, which operates on standard hardware at kHz sampling rate.

Event cameras asynchronously report timestamped changes in pixel intensity and offer advantages over conventional raster scan cameras in terms of low-latency, low redundancy sensing and high dynamic range. In recent years, much of research in event based vision has been focused on performing tasks such as optic flow estimation, moving object segmentation, feature tracking, camera rotation estimation and more, through contrast maximization. In contrast maximization, events are warped along motion trajectories whose parameters depend on the quantity being estimated, to some time t_ref. The parameters are then scored by some reward function of the accumulated events at t_ref. The versatility of this approach has lead to a flurry of research in recent years, but no in-depth study of the reward chosen during optimization has yet been made. In this work we examine the choice of reward used in contrast maximization, propose a classification of different rewards and show how a reward can be constructed that is more robust to noise and aperture uncertainty. We validate our work experimentally by predicting optical flow and comparing to ground-truth data.

In contrast to traditional cameras, whose pixels have a common exposure time, event-based cameras are novel bio-inspired sensors whose pixels work independently and asynchronously output intensity changes (called "events"), with microsecond resolution. Since events are caused by the apparent motion of objects, event-based cameras sample visual information based on the scene dynamics and are, therefore, a more natural fit than traditional cameras to acquire motion, especially at high speeds, where traditional cameras suffer from motion blur. However, distinguishing between events caused by different moving objects and by the camera's ego-motion is a challenging task. We present the first per-event segmentation method for splitting a scene into independently moving objects. Our method jointly estimates the event-object associations (i.e., segmentation) and the motion parameters of the objects (or the background) by maximization of an objective function, which builds upon recent results on event-based motion-compensation. We provide a thorough evaluation of our method on a public dataset, outperforming the state-of-the-art by as much as 10%. We also show the first quantitative evaluation of a segmentation algorithm for event cameras, yielding around 90% accuracy at 4 pixels relative displacement.