ED-DCFNet: An unsupervised encoder-decoder neural model for event-driven feature extraction and object tracking

Raz Ramon, Hadar Cohen-Duwek, Elishai Ezra Tsur

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Neuromorphic cameras feature asynchronous event-based pixel-level processing and are particularly useful for object tracking in dynamic environments. Current approaches for feature extraction and optical flow with high-performing hybrid RGB-events vision systems require large computational models and supervised learning, which impose challenges for embedded vision and require annotated datasets. In this work, we propose ED-DCFNet, a small and efficient (< 72k) unsupervised multi-domain learning framework, which extracts events-frames shared features without requiring annotations, with comparable performance. Furthermore, we introduce an open-sourced event and frame-based dataset that captures indoor scenes with various lighting and motion-type conditions in realistic scenarios, which can be used for model building and evaluation. The dataset is available at https://github.com/NBELab/UnsupervisedTracking.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024
PublisherIEEE Computer Society
Pages2191-2199
Number of pages9
ISBN (Electronic)9798350365474
DOIs
StatePublished - 2024
Event2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024 - Seattle, United States
Duration: 16 Jun 202422 Jun 2024

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024
Country/TerritoryUnited States
CitySeattle
Period16/06/2422/06/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • event camera
  • Neuromorphic vision
  • object tracking

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