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A fast binary pair-based video descriptor for action recognition
conference contribution
posted on 2016-01-01, 00:00 authored by R Leyva, V Sanchez, Chang-Tsun LiChang-Tsun LiInspired 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.
History
Event
IEEE Signal Processing Society. Conference (23rd : 2016 : Phoenix, Ariz.)Series
IEEE Signal Processing Society ConferencePagination
4185 - 4189Publisher
Institute of Electrical and Electronics EngineersLocation
Phoenix, Ariz.Place of publication
Piscataway, N.J.Publisher DOI
Start date
2016-09-25End date
2016-09-28ISSN
1522-4880ISBN-13
9781467399616Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2016, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
ICIP 2016 : Proceedings of the 2016 IEEE International Conference on Image ProcessingUsage metrics
Categories
No categories selectedKeywords
HistogramsFeature extractionThree-dimensional displaysSupport vector machinesTwo dimensional displaysPipelinesIntegrated opticsScience & TechnologyTechnologyComputer Science, Theory & MethodsEngineering, Electrical & ElectronicImaging Science & Photographic TechnologyComputer ScienceEngineeringAction recognitionbinary featuresBOV based modelsSCALEDENSE
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