Variational Pedestrian Detection

Yuang Zhang, Huanyu He, Jianguo Li, Yuxi Li, John See, Weiyao Lin*

*Corresponding author for this work

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

    26 Citations (Scopus)


    Pedestrian detection in a crowd is a challenging task due to a high number of mutually-occluding human instances, which brings ambiguity and optimization difficulties to the current IoU-based ground truth assignment procedure in classical object detection methods. In this paper, we develop a unique perspective of pedestrian detection as a variational inference problem. We formulate a novel and efficient algorithm for pedestrian detection by modeling the dense proposals as a latent variable while proposing a customized Auto-Encoding Variational Bayes (AEVB) algorithm. Through the optimization of our proposed algorithm, a classical detector can be fashioned into a variational pedestrian detector. Experiments conducted on CrowdHuman and CityPersons datasets show that the proposed algorithm serves as an efficient solution to handle the dense pedestrian detection problem for the case of single-stage detectors. Our method can also be flexibly applied to two-stage detectors, achieving notable performance enhancement.

    Original languageEnglish
    Title of host publicationProceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
    Number of pages10
    ISBN (Electronic)9781665445092
    Publication statusPublished - 2 Nov 2021
    Event2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition - Virtual, Online, United States
    Duration: 19 Jun 202125 Jun 2021

    Publication series

    NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    ISSN (Print)1063-6919


    Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition
    Abbreviated titleCVPR 2021
    Country/TerritoryUnited States
    CityVirtual, Online

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition


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