Regression based pose estimation with automatic occlusion detection and rectification

Ibrahim Radwan, Abhinav Dhall, Jyoti Dhall, Roland Goecke

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    10 Citations (Scopus)

    Abstract

    Human pose estimation is a classic problem in computer vision. Statistical models based on part-based modelling and the pictorial structure framework have been widely used recently for articulated human pose estimation. However, the performance of these models has been limited due to the presence of self-occlusion. This paper presents a learning-based framework to automatically detect and recover self-occluded body parts. We learn two different models: one for detecting occluded parts in the upper body and another one for the lower body. To solve the key problem of knowing which parts are occluded, we construct Gaussian Process Regression (GPR) models to learn the parameters of the occluded body parts from their corresponding ground truth parameters. Using these models, the pictorial structure of the occluded parts in unseen images is automatically rectified. The proposed framework outperforms a state-of-the-art pictorial structure approach for human pose estimation on 3 different datasets
    Original languageEnglish
    Title of host publicationProceedings - IEEE International Conference on Multimedia and Expo (ICME 2012)
    Place of PublicationUnited States
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages121-127
    Number of pages7
    ISBN (Print)9781467316590
    DOIs
    Publication statusPublished - 2012
    Event2012 IEEE International Conference on Multimedia and Expo (ICME) - Melbourne, Melbourne, Australia
    Duration: 9 Jul 201213 Jul 2012

    Conference

    Conference2012 IEEE International Conference on Multimedia and Expo (ICME)
    CountryAustralia
    CityMelbourne
    Period9/07/1213/07/12

    Fingerprint

    Computer vision
    Statistical Models

    Cite this

    Radwan, I., Dhall, A., Dhall, J., & Goecke, R. (2012). Regression based pose estimation with automatic occlusion detection and rectification. In Proceedings - IEEE International Conference on Multimedia and Expo (ICME 2012) (pp. 121-127). United States: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICME.2012.160
    Radwan, Ibrahim ; Dhall, Abhinav ; Dhall, Jyoti ; Goecke, Roland. / Regression based pose estimation with automatic occlusion detection and rectification. Proceedings - IEEE International Conference on Multimedia and Expo (ICME 2012). United States : IEEE, Institute of Electrical and Electronics Engineers, 2012. pp. 121-127
    @inproceedings{c0bd3e0d9f204fd08b51ba528f69d33a,
    title = "Regression based pose estimation with automatic occlusion detection and rectification",
    abstract = "Human pose estimation is a classic problem in computer vision. Statistical models based on part-based modelling and the pictorial structure framework have been widely used recently for articulated human pose estimation. However, the performance of these models has been limited due to the presence of self-occlusion. This paper presents a learning-based framework to automatically detect and recover self-occluded body parts. We learn two different models: one for detecting occluded parts in the upper body and another one for the lower body. To solve the key problem of knowing which parts are occluded, we construct Gaussian Process Regression (GPR) models to learn the parameters of the occluded body parts from their corresponding ground truth parameters. Using these models, the pictorial structure of the occluded parts in unseen images is automatically rectified. The proposed framework outperforms a state-of-the-art pictorial structure approach for human pose estimation on 3 different datasets",
    keywords = "Human pose estimation, Occlusion detection, Regression",
    author = "Ibrahim Radwan and Abhinav Dhall and Jyoti Dhall and Roland Goecke",
    year = "2012",
    doi = "10.1109/ICME.2012.160",
    language = "English",
    isbn = "9781467316590",
    pages = "121--127",
    booktitle = "Proceedings - IEEE International Conference on Multimedia and Expo (ICME 2012)",
    publisher = "IEEE, Institute of Electrical and Electronics Engineers",
    address = "United States",

    }

    Radwan, I, Dhall, A, Dhall, J & Goecke, R 2012, Regression based pose estimation with automatic occlusion detection and rectification. in Proceedings - IEEE International Conference on Multimedia and Expo (ICME 2012). IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 121-127, 2012 IEEE International Conference on Multimedia and Expo (ICME), Melbourne, Australia, 9/07/12. https://doi.org/10.1109/ICME.2012.160

    Regression based pose estimation with automatic occlusion detection and rectification. / Radwan, Ibrahim; Dhall, Abhinav; Dhall, Jyoti; Goecke, Roland.

    Proceedings - IEEE International Conference on Multimedia and Expo (ICME 2012). United States : IEEE, Institute of Electrical and Electronics Engineers, 2012. p. 121-127.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    TY - GEN

    T1 - Regression based pose estimation with automatic occlusion detection and rectification

    AU - Radwan, Ibrahim

    AU - Dhall, Abhinav

    AU - Dhall, Jyoti

    AU - Goecke, Roland

    PY - 2012

    Y1 - 2012

    N2 - Human pose estimation is a classic problem in computer vision. Statistical models based on part-based modelling and the pictorial structure framework have been widely used recently for articulated human pose estimation. However, the performance of these models has been limited due to the presence of self-occlusion. This paper presents a learning-based framework to automatically detect and recover self-occluded body parts. We learn two different models: one for detecting occluded parts in the upper body and another one for the lower body. To solve the key problem of knowing which parts are occluded, we construct Gaussian Process Regression (GPR) models to learn the parameters of the occluded body parts from their corresponding ground truth parameters. Using these models, the pictorial structure of the occluded parts in unseen images is automatically rectified. The proposed framework outperforms a state-of-the-art pictorial structure approach for human pose estimation on 3 different datasets

    AB - Human pose estimation is a classic problem in computer vision. Statistical models based on part-based modelling and the pictorial structure framework have been widely used recently for articulated human pose estimation. However, the performance of these models has been limited due to the presence of self-occlusion. This paper presents a learning-based framework to automatically detect and recover self-occluded body parts. We learn two different models: one for detecting occluded parts in the upper body and another one for the lower body. To solve the key problem of knowing which parts are occluded, we construct Gaussian Process Regression (GPR) models to learn the parameters of the occluded body parts from their corresponding ground truth parameters. Using these models, the pictorial structure of the occluded parts in unseen images is automatically rectified. The proposed framework outperforms a state-of-the-art pictorial structure approach for human pose estimation on 3 different datasets

    KW - Human pose estimation

    KW - Occlusion detection

    KW - Regression

    U2 - 10.1109/ICME.2012.160

    DO - 10.1109/ICME.2012.160

    M3 - Conference contribution

    SN - 9781467316590

    SP - 121

    EP - 127

    BT - Proceedings - IEEE International Conference on Multimedia and Expo (ICME 2012)

    PB - IEEE, Institute of Electrical and Electronics Engineers

    CY - United States

    ER -

    Radwan I, Dhall A, Dhall J, Goecke R. Regression based pose estimation with automatic occlusion detection and rectification. In Proceedings - IEEE International Conference on Multimedia and Expo (ICME 2012). United States: IEEE, Institute of Electrical and Electronics Engineers. 2012. p. 121-127 https://doi.org/10.1109/ICME.2012.160