EvAn: Neuromorphic Event-Based Sparse Anomaly Detection.

ABSTRACT: Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events. This principle results in significant advantages over conventional cameras, such as low power utilization, high dynamic range, and no motion blur. Moreover, by design, such cameras encode only the relative motion between the scene and the sensor and not the static background to yield a very sparse data structure. In this paper, we leverage these advantages of an event camera toward a critical vision application-video anomaly detection. We propose an anomaly detection solution in the event domain with a conditional Generative Adversarial Network (cGAN) made up of sparse submanifold convolution layers. Video analytics tasks such as anomaly detection depend on th

SUBMITTER: Annamalai L 

PROVIDER: S-EPMC8358807 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature