Superpixel-based statistical anomaly detection for sense and avoid

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)
401 Downloads (Pure)


This paper presents a novel preprocessing method for detecting small objects of interest within a high-resolution image, applied to the problem of visually detecting possible aircraft collisions (Sense and Avoid) for UAV platforms. The method is based on superpixel image segmentation combined with subsequent statistical analysis and anomaly detection. The existence of a possible target within a superpixel is described in terms of how it affects the local superpixel statistics and this signature statistical profile is consequently used to identify regions of interest throughout the image. The approach eliminates upwards of 90% of the total image area, significantly reducing the workload of further processing stages.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing (ICIP 2015)
Subtitle of host publicationProceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781479983391
ISBN (Print)9781479983407
Publication statusPublished - Jan 2016
Event2015 IEEE International Conference on Image Processing (ICIP) - Quebec City, ON, Canada
Duration: 27 Sept 201530 Sept 2015


Conference2015 IEEE International Conference on Image Processing (ICIP)
CityQuebec City, ON


  • Anomaly Detection
  • Sense and Avoid
  • Statistical Detector
  • Superpixels
  • Context
  • Measurement
  • Standards
  • Aircraft
  • Visualization
  • Detectors
  • Image segmentation


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