Asynchronous Multi-Hypothesis Tracking of Features with Event Cameras

With the emergence of event cameras, increasing research effort has been focusing on processing the asynchronous stream of events. With each event encoding a discrete intensity change at a particular pixel, uniquely time-stamped with high accuracy, this sensing information is so fundamentally different to the data provided by traditional frame-based cameras that most of the well-established vision algorithms are not applicable. Inspired by the need of effective event-based tracking, this paper addresses the tracking of generic patch features relying solely on events, while exploiting their asynchronicity and high-temporal resolution. The proposed approach outperforms the state-of-the-art in event-based feature tracking on well-established event camera datasets, retrieving longer and more accurate feature tracks at higher a frequency. Considering tracking as an optimization problem of matching the current view to a feature template, the proposed method implements a simple and efficient technique that only requires the evaluation of a discrete set of tracking hypotheses.