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 2016 - Proceedings
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
CountryUnited States
Internet address

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