Event Vision-based Corner Detection with Count-normalized Multi-Layer Perceptron and Throughput Indicator

Abstract

The advancement of event cameras has sparked a revolution in imaging technology, presenting exciting opportunities for vision-based measurement tasks. Event cameras operate on an innovative asynchronous imaging principle, which offers several advantages over traditional cameras, including ultra-high dynamic range and exceptional temporal resolution. As a result, event cameras excel in challenging environments characterized by motion blur, overexposure, or underexposure, outperforming conventional frame-based cameras. However, the asynchronous signal streams generated by event cameras pose compatibility challenges with existing vision-based measurement algorithms. This paper focuses specifically on corner detection, a critical vision-based measurement task, tailored for event cameras. To address this challenge, we propose novel corner detectors that leverage advanced optimization techniques, including enhanced time surface representations, multi-layer perceptron classifiers, and an innovative throughput mechanism. Through rigorous experimentation, our method consistently shows lower projection errors compared to state-of-the-art methods across all datasets while also maintaining longer tracking times in low-textured scenarios. Specifically, our CMLP and CMLP-T methods achieve an average valid tracking rate of 83.38% and 84.65%, respectively, on the DAVIS240C dataset collection, surpassing all existing methods. We validate the effectiveness of our proposed corner detectors by demonstrating their enhanced performance compared to state-of-the-art methods. Furthermore, our work contributes to the application of machine learning in event signal processing for vision-based measurement tasks, providing insights into optimizing models for the unique characteristics of event cameras.