ACE: An Efficient Asynchronous Corner Tracker for Event Cameras

The emergence of bio-inspired event cameras has opened up new exciting possibilities in high-frequency tracking, overcoming some of the limitations of traditional frame-based vision (e.g. motion blur during high-speed motions or saturation in scenes with high dynamic range). As a result, research has been focusing on the processing of their unusual output: an asynchronous stream of events. With the majority of existing techniques discretizing the event-stream into frame-like representations, we are yet to harness the true power of these cameras. In this paper, we propose the ACE tracker: a purely asynchronous framework to track corner-event features. Evaluation on benchmarking datasets reveals significant improvements in accuracy and computational efficiency in comparison to state-of-the-art event-based trackers. ACE achieves robust performance even in challenging scenarios, where traditional frame-based vision algorithms fail.