Modernised Reduction: Adapting the ROT tree

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


Neuromorphic vision data offers a new means of evaluating digitise spatial and temporal representations of evolving scene dynamics. Reduction-Over-Time (ROT) trees have seen growing popularity as a medium for storing and operating over the temporally asynchronous data produced from neuromorphic sensors given their 1-Dnature, spatial preservation, and speed. In this paper we propose a variation of the ROT tree called R-ROT which allows for greater adaptability within structure when compared to the originally proposed ROT tree using adaptive self pruning. The R-ROT structure is evaluated against the original ROT model and is shown to achieve high accuracy results in shorter time across a widely popular benchmark database.
Original languageEnglish
Title of host publicationInternational Conference on Intelligent Autonomous Systems
Publication statusAccepted/In press - 2023
Event18th International Conference on Intelligent Autonomous Systems - Suwon, Korea, Democratic People's Republic of
Duration: 4 Jul 20237 Jul 2023


Conference18th International Conference on Intelligent Autonomous Systems
Abbreviated titleIAS18-2023
Country/TerritoryKorea, Democratic People's Republic of
Internet address


  • smart sensors and actuators
  • smart factory
  • neuromorphic data


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