Illumination invariant sequential filtering human tracking

Yifan Lu*, Dan Xu, Lei Wang, Richard Hartley, Hongdong Li

*Corresponding author for this work

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

    2 Citations (Scopus)

    Abstract

    Many tracking problems can be efficiently solved by the Altering technique. Linear filter methods (e.g. Kaiman Filter) have shown their success and optimally in many linear settings with Gaussian noises. However, they expose inefficiency and weakness in the general nonlinear and high dimensional setting (e.g. human tracking). While, the advancement of Sequential Importance Re-sampling with Simulated Annealing has shown it is capable of handling nonlinearity and high dimensionality of human tracking. However, its performance is often affected by lighting variations and noises from silhouette segmentation. The proposed approach incorporates a textured human body template to annealed sequential filtering, and uses the illumination invariant CIELab formula to evaluate the observation likelihood so that influences of lighting changes and noises are minimised. Experiments with the benchmark HumanEval dataset demonstrate encouraging improvements over traditional Sequential Importance Re-sampling and the silhouette based method.

    Original languageEnglish
    Title of host publication2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
    Pages2133-2138
    Number of pages6
    DOIs
    Publication statusPublished - 2010
    Event2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China
    Duration: 11 Jul 201014 Jul 2010

    Publication series

    Name2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
    Volume4

    Conference

    Conference2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
    Country/TerritoryChina
    CityQingdao
    Period11/07/1014/07/10

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