Differential EMD tracking

Qi Zhao, Shane Brennan, Hai Tao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

29 Scopus citations


Illumination changes cause object appearance to change drastically and many existing tracking algorithms lack the capability to handle this problem. The Earth Mover's Distance (EMD) is a similarity measure that is more robust against illumination changes. However, EMD is computationally expensive and we therefore propose the Differential EMD (DEMD) algorithm which computes the derivative of the EMD with respect to the object location so that the EMD does not need to be computed for every location in the tracking window. The fast differential formula is derived based on the sensitivity analysis of the simplex method as applied to the EMD formula. To further reduce the computation, signatures, i.e., variable-size descriptions of distributions, are employed as an object representation. The new algorithm models local background scenes as well as foreground objects to handle scale changes in a principled way. Extensive quantitative evaluation of the proposed algorithm has been carried out using benchmark sequences and the improvement over the standard Mean Shift tracker is demonstrated.

Original languageEnglish (US)
Title of host publication2007 IEEE 11th International Conference on Computer Vision, ICCV
StatePublished - Dec 1 2007
Event2007 IEEE 11th International Conference on Computer Vision, ICCV - Rio de Janeiro, Brazil
Duration: Oct 14 2007Oct 21 2007


Other2007 IEEE 11th International Conference on Computer Vision, ICCV
CityRio de Janeiro


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