Reducing-Over-Time Tree for Event-based Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a novel Reducing-Over-Time (ROT) binary tree structure for event-based vision data and sub- types of the tree structure. A framework is presented using ROT, that takes advantage of the self-balancing and self-pruning nature of the tree structure to extract spatial-temporal information. The ROT framework is paired with an established motion classification technique and performance is evaluated against other state-of-the-art techniques using four datasets. Additionally, the ROT framework as a processing platform is compared with other event-based vision processing platforms in terms of memory usage and is found to be one of the most memory efficient platforms available.
Original languageEnglish
Title of host publicationThe 25th IEEE International Conference on Pattern Recognition
Number of pages8
Publication statusAccepted/In press - 21 Jun 2020
Event The 25th IEEE International Conference on Pattern Recognition - Milan, Italy
Duration: 10 Jan 202115 Jan 2021
Conference number: 25th
https://www.micc.unifi.it/icpr2020/

Conference

Conference The 25th IEEE International Conference on Pattern Recognition
Abbreviated titleICPR2020
CountryItaly
CityMilan
Period10/01/2115/01/21
Internet address

Keywords

  • Bio-inspired
  • Neuromorphic
  • Computer Vision
  • Feature Extraction
  • Corner Detection
  • Edge Detection
  • Motion Recognition

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  • Cite this

    Harrigan, S., Coleman, S., Kerr, D., Pratheepan, Y., Fang, Z., & Wu, C. (Accepted/In press). Reducing-Over-Time Tree for Event-based Data. In The 25th IEEE International Conference on Pattern Recognition