An Application-Driven Survey on Event-Based Neuromorphic Computer Vision

Abstract

Traditional frame-based cameras, despite their effectiveness and usage in computer vision, exhibit limitations such as high latency, low dynamic range, high power consumption, and motion blur. For two decades, researchers have explored neuromorphic cameras, which operate differently from traditional frame-based types, mimicking biological vision systems for enhanced data acquisition and spatio-temporal resolution. Each pixel asynchronously captures intensity changes in the scene above certain user-defined thresholds, and streams of events are captured. However, the distinct characteristics of these sensors mean that traditional computer vision methods are not directly applicable, necessitating the investigation of new approaches before being applied in real applications. This work aims to fill existing gaps in the literature by providing a survey and a discussion centered on the different application domains, differentiating between computer vision problems and whether solutions are better suited for or have been applied to a specific field. Moreover, an extensive discussion highlights the major achievements and challenges, in addition to the unique characteristics, of each application field.