Superevents : towards native semantic segmentation for event-based cameras

Weng Fei Low, Ankit Sonthalia, Zhi Gao, Andre van Schaik, Bharat Ramesh

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

1 Citation (Scopus)

Abstract

![CDATA[Most successful computer vision models transform low-level features, such as Gabor filter responses, into richer representations of intermediate or mid-level complexity for downstream visual tasks. These mid-level representations have not been explored for event cameras, although it is especially relevant to the visually sparse and often disjoint spatial information in the event stream. By making use of locally consistent intermediate representations, termed as superevents, numerous visual tasks ranging from semantic segmentation, visual tracking, depth estimation shall benefit. In essence, superevents are perceptually consistent local units that delineate parts of an object in a scene. Inspired by recent deep learning architectures, we present a novel method that employs lifetime augmentation for obtaining an event stream representation that is fed to a fully convolutional network to extract superevents. Our qualitative and quantitative experimental results on several sequences of a benchmark dataset highlights the significant potential for event-based downstream applications.]]
Original languageEnglish
Title of host publicationProceedings of the 2021 International Conference on Neuromorphic Systems (ICONS 2021), Knoxville, TN, USA July 27 - 29, 2021
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Print)9781450386913
DOIs
Publication statusPublished - 2021
EventInternational Conference on Neuromorphic Systems -
Duration: 27 Jul 2021 → …

Conference

ConferenceInternational Conference on Neuromorphic Systems
Period27/07/21 → …

Fingerprint

Dive into the research topics of 'Superevents : towards native semantic segmentation for event-based cameras'. Together they form a unique fingerprint.

Cite this