Fast inference of contaminated data for real time object tracking

Hao Zhu*, Yi Li

*Corresponding author for this work

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

    Abstract

    The online object tracking is a challenging problem because any useful approach must handle various nuisances including illumination changes and occlusions. Though a lot of work focus on observation models by employing sophisticated approaches for contaminated data, they commonly assume that the samples for updating observation model are uncorrupted or can be restored in updating. For instance, in particle filter based approaches every particle has to be restored for each frame, which is time-consuming and unstable. In this paper, we propose a novel scheme to decouple the observation model and its update in a particle filtering framework. Our efficient observation model is used to effectively select the most similar candidate from all particles only, by analyzing the principal component analysis (PCA) reconstruction with L1 regularization. In order to handle the contaminated samples while updating observation model, we adopt on an online robust PCA during the update of observation model. Our qualitative and quantitative evaluations on challenging dataset demonstrate that the proposed scheme is competitive to several sophisticated state of the art methods, and it is much faster.

    Original languageEnglish
    Title of host publicationComputer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers
    EditorsDaniel Cremers, Hideo Saito, Ian Reid, Ming-Hsuan Yang
    PublisherSpringer Verlag
    Pages275-289
    Number of pages15
    ISBN (Electronic)9783319168135
    DOIs
    Publication statusPublished - 2015
    Event12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore
    Duration: 1 Nov 20145 Nov 2014

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9007
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference12th Asian Conference on Computer Vision, ACCV 2014
    Country/TerritorySingapore
    CitySingapore
    Period1/11/145/11/14

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