Salient Obstacle Avoidance for Robotic Systems

Research output: Contribution to conferencePaper

10 Downloads (Pure)

Abstract

Salient object detection is a very active area of interest, based on human selective attention which shifts our focus across prominent areas in a scene. Many models have been created and achieved impressive accuracies, however, this often comes at a high computational efficiency cost making them less attractive for real-time robotic systems. We develop a novel salient object detection algorithm and investigate the trade-off between accuracy and computation time by comparing pixel-based and region-based processing. Salient object detection has been used in a variety of applications, however, this work is focused on developing an algorithm considering obstacle avoidance for a robot operating in a cluttered and dynamic environment. The strategy is evaluated on the publicly available MSRA10K salient object dataset, against three current state-of-the-art methods.
Original languageEnglish
Pages57
Number of pages64
Publication statusPublished - 29 Jul 2018
EventIrish Machine Vision and Image Processing Conference - Belfast, United Kingdom
Duration: 29 Aug 201831 Aug 2018

Conference

ConferenceIrish Machine Vision and Image Processing Conference
Abbreviated titleIMVIP
CountryUnited Kingdom
Period29/08/1831/08/18

Keywords

  • Saliency
  • SLIC
  • Obstacle Avoidance

Fingerprint Dive into the research topics of 'Salient Obstacle Avoidance for Robotic Systems'. Together they form a unique fingerprint.

  • Cite this

    Cooley, C., Coleman, S., Gardiner, B., & Scotney, B. (2018). Salient Obstacle Avoidance for Robotic Systems. 57. Paper presented at Irish Machine Vision and Image Processing Conference, United Kingdom.