A fast binary pair-based video descriptor for action recognition

Roberto Leyva, Victor Sanchez, Chang-Tsun Li

    Research output: Book chapter/Published conference paperConference paperpeer-review

    6 Citations (Scopus)
    4 Downloads (Pure)


    Inspired by the binary-based descriptors (e.g. LBP, ALOHA, FREAK, BRISK), we propose the 3D Binary Pair Differences (3DBPD) video descriptor for action recognition. By comparing several spatio-temporal sub-regions around interests points, our descriptor is a feature vector with a dimensionality of up to 30% smaller than that of existing state-of-the-art descriptors. We demonstrate the effectiveness of the 3DBPD descriptor for action recognition with a SVM classifier and a simple Bag Of Video Words (BOV) generated using k-means. The proposed descriptor has very competitive recognition rates compared to other state-of-the-art descriptors, with an outstanding performance in terms of speed. Additionally, the 3DBPD descriptor requires a small codebook compared to those required by existing BOV-based descriptors.
    Original languageEnglish
    Title of host publication2016 IEEE International Conference on Image Processing, (ICIP)
    Place of PublicationUnited States
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Number of pages5
    ISBN (Print)9781467399616
    Publication statusPublished - 2016
    Event2016 23rd IEEE International Conference on Image Processing: ICIP 2016 - Phoenix Convention Center, Phoenix, United States
    Duration: 25 Sep 201628 Sep 2016
    Conference number: 125190


    Conference2016 23rd IEEE International Conference on Image Processing
    Country/TerritoryUnited States
    Internet address


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