Dynamic Resource-aware Corner Detection for Bio-inspired Vision Sensors

TL;DR

This paper introduces TLF-Harris, a resource-efficient, real-time corner detection algorithm for event-based cameras that filters events to improve accuracy and performance on embedded systems.

Contribution

The paper presents a novel filtering strategy combined with an approximation of Harris detection, enabling high-accuracy corner detection with reduced computational load on embedded platforms.

Findings

Achieves 59% average execution time savings over conventional Harris.

Outperforms eFAST, eHarris, and FA-Harris in real-time performance.

Surpasses Arc* in detection accuracy.

Abstract

Event-based cameras are vision devices that transmit only brightness changes with low latency and ultra-low power consumption. Such characteristics make event-based cameras attractive in the field of localization and object tracking in resource-constrained systems. Since the number of generated events in such cameras is huge, the selection and filtering of the incoming events are beneficial from both increasing the accuracy of the features and reducing the computational load. In this paper, we present an algorithm to detect asynchronous corners from a stream of events in real-time on embedded systems. The algorithm is called the Three Layer Filtering-Harris or TLF-Harris algorithm. The algorithm is based on an events' filtering strategy whose purpose is 1) to increase the accuracy by deliberately eliminating some incoming events, i.e., noise, and 2) to improve the real-time performance of the system, i.e., preserving a constant throughput in terms of input events per second, by discarding unnecessary events with a limited accuracy loss. An approximation of the Harris algorithm, in turn, is used to exploit its high-quality detection capability with a low-complexity implementation to enable seamless real-time performance on embedded computing platforms. The proposed algorithm is capable of selecting the best corner candidate among neighbors and achieves an average execution time savings of 59 % compared with the conventional Harris score. Moreover, our approach outperforms the competing methods, such as eFAST, eHarris, and FA-Harris, in terms of real-time performance, and surpasses Arc* in terms of accuracy.