Good sample consensus estimation of 2D-homographies for vehicle movement detection from thermal videos

Eckart Michaelsen, Uwe Stilla

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

In this contribution we describe a method to assess the activity of vehicles based on airborne image sequences taken by an infrared camera. Active vehicles often appear as a configuration of a dark and a bright spot close to each other. The sensor movement is inferred from image sequences. Due to the fast velocity of the platform estimations of vehicle movements require a precise measurement of the sensor movement. The camera may be tilted with the aircraft giving arbitrarily oblique views. Camera movements are treated as projective 2D-homographies. For the search of a subset of image point correspondences that is free of outliers and gives a precise estimate of the movement we use a production system implementing good sample consensus (GSAC). This new method is derived from the well known RANSAC-decisions and improves them by preferring good samples to random samples. As assessment criterion for minimal samples the area of the smallest triangle in the sample is used. We motivate the criterion for the quality of samples by error propagation through the estimated homography. A comparison is made with other robust estimation techniques namly RANSAC and iterative re-weighted least squares.

Original languageEnglish
Pages (from-to)125-130
Number of pages6
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume34
StatePublished - 2003
Externally publishedYes
Event2003 ISPRS Workshop on Photogrammetric Image Analysis, PIA 2003 - Munich, Germany
Duration: 17 Sep 200319 Sep 2003

Keywords

  • Infrared surveillance
  • Machine vision
  • Object recognition
  • Robust estimation
  • Urban areas
  • Vehicles

Fingerprint

Dive into the research topics of 'Good sample consensus estimation of 2D-homographies for vehicle movement detection from thermal videos'. Together they form a unique fingerprint.

Cite this