eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-

TL;DR

eCDT introduces a novel clustering-based method for simultaneous feature detection and tracking in event camera data, maintaining high temporal resolution and outperforming existing approaches in tracking longevity.

Contribution

The paper presents eCDT, a new event data-friendly method that combines clustering and descriptor matching for real-time, high-resolution feature detection and tracking without frame conversion.

Findings

30% improvement in feature tracking ages

Low error rate comparable to state-of-the-art

Automatic simultaneous detection and tracking

Abstract

Contrary to other standard cameras, event cameras interpret the world in an entirely different manner; as a collection of asynchronous events. Despite event camera's unique data output, many event feature detection and tracking algorithms have shown significant progress by making detours to frame-based data representations. This paper questions the need to do so and proposes a novel event data-friendly method that achieve simultaneous feature detection and tracking, called event Clustering-based Detection and Tracking (eCDT). Our method employs a novel clustering method, named as k-NN Classifier-based Spatial Clustering and Applications with Noise (KCSCAN), to cluster adjacent polarity events to retrieve event trajectories.With the aid of a Head and Tail Descriptor Matching process, event clusters that reappear in a different polarity are continually tracked, elongating the feature tracks. Thanks to our clustering approach in spatio-temporal space, our method automatically solves feature detection and feature tracking simultaneously. Also, eCDT can extract feature tracks at any frequency with an adjustable time window, which does not corrupt the high temporal resolution of the original event data. Our method achieves 30% better feature tracking ages compared with the state-of-the-art approach while also having a low error approximately equal to it.