Bayesian fusion of thermal and visible spectra camera data for mean shift tracking with rapid background adaptation

Rustam Stolkin, David Rees, Mohammed Talha, Ionut Florescu

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

16 Scopus citations

Abstract

This paper presents a method for optimally combining pixel information from thermal imaging and visible spectrum colour cameras, for tracking an arbitrarily shaped deformable moving target. The tracking algorithm rapidly re-learns its background models for each camera modality from scratch at every frame. This enables, firstly, automatic adjustment of the relative importance of thermal and visible information in decision making, and, secondly, a degree of 'camouflage target' tracking by continuously re-weighting the importance of those parts of the target model that are most distinct from the present background at each frame. Furthermore, this very rapid background adaptation ensures robustness to rapid camera motion. The combination of thermal and visible information is applicable to any target, but particularly useful for people tracking. The method is also important in that it can be readily extended for fusion of data from arbitrarily many imaging modalities.

Original languageEnglish
Title of host publicationIEEE SENSORS 2012 - Proceedings
DOIs
StatePublished - 2012
Event11th IEEE SENSORS 2012 Conference - Taipei, Taiwan, Province of China
Duration: 28 Oct 201231 Oct 2012

Publication series

NameProceedings of IEEE Sensors

Conference

Conference11th IEEE SENSORS 2012 Conference
Country/TerritoryTaiwan, Province of China
CityTaipei
Period28/10/1231/10/12

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